Archive / 2026-08-16
August 16, 2026
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Multi-Agent Systems Are Failing in Ways No Single-Model Eval Catches
An OpenAI red-team exercise, an Anthropic risk report, and a Science Advances study all describe the same phenomenon this week: agents doing things in groups that none of them would do alone. The eval industry is still mostly built to check one model at a time.
TL;DRMultiple reports this week — an OpenAI-Hugging Face red-team breach, an Anthropic sabotage finding, a 1,000-agent consensus study — describe group-level agent failures that single-model evals don't catch, while the $915M eval industry is still arguing over rankings for one model at a time.
Read the full post → https://engineerious.com/blog/2026-08-16-multi-agent-systems-are-failing-in-ways-no-single-model-eval
News
View all news →vectoral.com1 month ago326 ptsView detailsJoin discussion
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On AI regulation and messaging
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What happens when an LLM never sees material beyond fifth grade?
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Young People Hate AI CEOs So Passionately That It's Almost Hard to Believe
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AGI-64 Brings Sierra Adventures to the Commodore 64
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A U.S. Strategy to Prevent the Creation of Mirror Life
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MathCode, Mathematical Coding Agent
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Red queen hypothesis – A new way forward for self-improving AI
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The Case Against Formal Verification, 50 Years Later
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The Iran War Is a Whole New Level of Quagmire for the US
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Show HN: A public AI whose memory is shared across all users
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The Trumps' Crypto Project Just Got One Step Closer to Becoming a Bank
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If your agent commits a crime, who is responsible?
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Anthropic CEO says the way for AI to win over the public is to cure cancer
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Show HN: Grafana agent observability for Hermes Agent
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US Air Force jet engine manufacturing plagued by 'significant challenges'
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The 'Country Hicks' Who Refused $26M from an AI Data Center
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Stripe will reportedly acquire AI gateway startup OpenRouter for $7B+
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The AI store manager fired its first human
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AI is not just one bubble, strategist says – but a 'rolling sequence of bubbles'
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Anthropic CEO says AI backlash is 'fundamentally a crisis of trust'
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- Primary source
New policy ideas for the Intelligence Age
OpenAI funds 14 independent projects exploring new AI policy ideas to expand economic opportunity and strengthen societal resilience in the Intelligence Age.
openai.com1 month agoView details
OpenAI reportedly disbanded its preparedness team
According to the Financial Times, OpenAI disbanded its preparedness team at the end of last month. The job of the preparedness team was to assess if models posed serious risks and develop ways to mitigate those risks. (You know, like the possibility that it could go rogue and hack another company.) OpenAI spokesperson…
theverge.com1 month agoView details
ChatGPT’s Computer History tracks your clicks and keystrokes
ChatGPT's desktop app on macOS has a new feature called Computer History that turns your actions into training data, learning how you work, suggesting automations, and even picking up tasks you left half done. It uses your activity to build a timeline that ChatGPT and Codex can reference when you make a request. The f…
theverge.com1 month agoView details
Rogue AI aren’t science fiction anymore
This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on AI safety, follow Robert Hart. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started It all started in July, when one of OpenAI's autonomous AI agents went rog…
theverge.com1 month agoView details
arXiv:2608.13987v1 Announce Type: new Abstract: Nanbeige4.2-3B is a 3B-parameter agentic model built around a Looped Transformer (LT) that reuses one stack of layers for a second forward pass, adding effective depth without additional parameters. Evaluated on Apple Silicon (MPS), we identify five independent bugs whic…
arxiv.org1 month agoView details
Retrieval Grounding Latent Reasoning for Dense Retrieval
arXiv:2608.14107v1 Announce Type: new Abstract: Reasoning-intensive retrieval requires text representations to capture not only semantic similarity, but also the reasoning needed to determine relevance under a given retrieval instruction. Existing reasoning-enhanced embedding models improve retrieval by incorporating…
arxiv.org1 month agoView details
StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
arXiv:2608.13717v1 Announce Type: new Abstract: Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We present StreamHear, a semi-supervised pipeline that adapts a pretrained streaming student by…
arxiv.org1 month agoView details
Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
arXiv:2608.13760v1 Announce Type: new Abstract: Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors? This distinction is important because reasoning-oriented training can make traces look more deliberative without amplifying th…
arxiv.org1 month agoView details
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
arXiv:2608.14375v1 Announce Type: cross Abstract: Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering assumes that a message likely to be correct is also worth keeping. Yet a wrong answer can contain a useful decomposit…
arxiv.org1 month agoView details
Removing Temporal Note Redundancy Improves Multimodal Reinforcement Learning for Medicine
arXiv:2608.14157v1 Announce Type: new Abstract: Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient's condition evolves. While reinforcement learning (RL) offers a promising framework for optimizing these sequential decisions, standard appro…
arxiv.org1 month agoView details
Not All Tokens Are Equal: Inflation-Aware Routing for Agentic LLM Systems
arXiv:2608.13571v1 Announce Type: new Abstract: When a language model fails to answer a query on the first attempt, an agentic system retries, consuming additional tokens each time. This retry overhead creates a gap between what a model's per-token price implies and what a full workflow actually costs. We call this ga…
arxiv.org1 month agoView details
arXiv:2608.13787v1 Announce Type: cross Abstract: AI agents increasingly act on their users' behalf, handling tasks such as scheduling meetings, comparing offers, and haggling over prices. These principal-driven tasks routinely place the agent across from a counterpart (another user's agent, a seller, a recruiter) who…
arxiv.org1 month agoView details
arXiv:2608.13786v1 Announce Type: cross Abstract: Large language model (LLM) chatbots are increasingly used to answer clinical questions with citations to relevant clinical studies. Prior research has largely focused on citation fabrication, leaving a gap in evaluating the quality of retrieved studies and the factors…
arxiv.org1 month agoView details
arXiv:2608.13754v1 Announce Type: new Abstract: The EU AI Act requires providers of high-risk systems to file technical documentation describing how the system reaches its decisions. Mechanistic interpretability is the obvious source of such evidence, and circuit discovery is its most developed instrument. We ask whet…
arxiv.org1 month agoView details
Local and Global Regimes of Geometric Complexity in Language Model Representations
arXiv:2608.14361v1 Announce Type: new Abstract: Intrinsic dimensionality (ID) is widely used to probe the representational complexity of language models, but it remains unclear whether ID differences reflect properties of language itself or artefacts of how the underlying dataset was constructed. In this paper, we foc…
arxiv.org1 month agoView details
Agent-Orchestration in Autonomous Chip Design
arXiv:2608.14035v1 Announce Type: new Abstract: Recent developments in large language models (LLMs) and tool-using agents encourage people to explore the potential of using agents in chip design. The core question is what kind of AI we really need in such a sophisticated industry. To this end, we bring the idea of mod…
arxiv.org1 month agoView details
arXiv:2608.13708v1 Announce Type: new Abstract: Automatically generating textbook-grounded assessment items can reduce science teachers' workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation, lack safeguards against weak evidence, and are il…
arxiv.org1 month agoView details
GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis
arXiv:2608.13741v2 Announce Type: new Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation. However, existing text-conditioned generators either take caption embeddings frozen from off-the-shelf text encoders, or adapt the encoder end-t…
arxiv.org1 month agoView details
Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation
arXiv:2608.14457v1 Announce Type: new Abstract: The majority of work on summarization evaluation focuses on general summary quality (e.g., ROUGE, BERTScore) or specific desired properties (e.g., readability, factuality). However, these metrics fail to measure the utility of a summary to an individual user. For example…
arxiv.org1 month agoView details
Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement
arXiv:2608.13767v1 Announce Type: new Abstract: Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement. Although end-to-end layout generators accelerate initial placement and routing, they still require experts to manually tune layout optimization parameters with…
arxiv.org1 month agoView details
Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference
arXiv:2608.13863v1 Announce Type: new Abstract: Deep neural network (DNN) inference on mobile devices often incurs high latency and energy consumption due to limited computing and memory resources. To enable energy-efficient DNN inference, most existing studies focus on dynamic voltage and frequency scaling (DVFS) for…
arxiv.org1 month agoView details
arXiv:2608.14109v1 Announce Type: new Abstract: Autonomous LLM agents are increasingly deployed in complex real-world workflows, yet they remain vulnerable to runtime behavioral drift, a silent deviation from the original task that can lead to irreversible side effects on external systems. Existing approaches address…
arxiv.org1 month agoView details
arXiv:2608.13568v1 Announce Type: new Abstract: Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call from a comment. Semantic retrieval via the Language Server Protocol (LSP) is precise and type…
arxiv.org1 month agoView details
arXiv:2608.14055v1 Announce Type: new Abstract: Authoritative scientific knowledge in geoscience remains largely trapped in legacy monographs and historical literature, where unstructured text and complex layouts hinder computational access. We introduce HERMES, a scalable multi-agent framework that extracts structure…
arxiv.org1 month agoView details
MINT: A Universal Zero-Shot Predictor for Transaction Data
arXiv:2608.14198v1 Announce Type: cross Abstract: Banks analyse sequential financial transaction data to perform many tasks, including fraud prevention, credit risk assessment and offer personalization. To improve the predictive accuracy of these tasks, Payments Foundation Models encode transaction sequence data as ri…
arxiv.org1 month agoView details
Buy the Rumor, Sell the News: When Is News Priced In?
arXiv:2608.14014v1 Announce Type: new Abstract: Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it. Whether the claims hold,…
arxiv.org1 month agoView details
GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings
arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for improving the reasoning capabilities of pretrained language models but current studies remain heavily English-centric. W…
arxiv.org1 month agoView details
arXiv:2608.14074v1 Announce Type: new Abstract: AI agents increasingly act on external systems through standardized tool-calling protocols such as the Model Context Protocol (MCP), yet no infrastructure layer constrains their actions to what a principal has verifiably authorized: authorization logic lives in applicati…
arxiv.org1 month agoView details
Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost
arXiv:2608.14125v1 Announce Type: new Abstract: LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints and the goal. However, LeWM has two limitations. First, during training, it learns local nex…
arxiv.org1 month agoView details
arXiv:2608.13835v1 Announce Type: new Abstract: Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning persists. We evaluate 120 topics from CoNTM and DLDA across NYT, DBLP, and arXiv, using three human annotators and Low, Med…
arxiv.org1 month agoView details
arXiv:2608.14003v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks through extended chain-of-thought generation, but incur substantial computational costs during inference. In production settings, batched inference is essential for high throughput, yet the existin…
arxiv.org1 month agoView details
SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning
arXiv:2608.14277v1 Announce Type: new Abstract: On-policy distillation (OPD) offers a promising way to transfer reasoning capabilities from stronger teacher models, but applying it to long-context reasoning teachers and short-context students introduces practical challenges, including tokenizer mismatch, teacher-stude…
arxiv.org1 month agoView details
QuaSAR: Quantization Compensation via Stable Activation-Aware Rank Truncation
arXiv:2608.14149v1 Announce Type: new Abstract: Recent training-free post-training quantization methods restore model accuracy through closed-form residual compensation. To constrain additional model storage overhead, several existing methods gate layer selection by goodness-of-fit, retaining only those layers whose c…
arxiv.org1 month agoView details
arXiv:2608.13706v1 Announce Type: new Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting…
arxiv.org1 month agoView details
Depth-Aware Sensitivity Analysis of Mixture-of-Experts Models via Magnitude-Based Expert Masking
arXiv:2608.13565v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures scale large language models (LLMs) while preserving computational efficiency through sparse activation. Despite their widespread adoption, the relative importance of individual MoE layers remains insufficiently characterized, partic…
arxiv.org1 month agoView details
arXiv:2608.14179v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable reasoning and generative capabilities, motivating their use as universal reasoning engines for perception. While modern approaches such as vision-language models (VLMs) have attempted to incorporate reasoning capabilitie…
arxiv.org1 month agoView details
A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images
arXiv:2608.14075v1 Announce Type: new Abstract: Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret. The ALD/E-ImageMiner benchmark and ICDAR 2026 Competition on Information Extraction from Atomic Layer Dep…
arxiv.org1 month agoView details
arXiv:2608.14089v1 Announce Type: cross Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather than the deployer's desired policy, and their performance degrades as deployment traffic evolves. We present Regime-Cond…
arxiv.org1 month agoView details
arXiv:2608.14122v1 Announce Type: new Abstract: Production scheduling in complex manufacturing environments is challenging when sequence-dependent setup times, stochastic disturbances, and due-date constraints must be addressed simultaneously. While reinforcement learning (RL) methods have shown promising results in r…
arxiv.org1 month agoView details
BiasTrace: Linking Reasoning Behaviours to Biased Outputs in LLMs
arXiv:2608.14161v1 Announce Type: new Abstract: LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications. While prior work has made progress in measuring and mitigating bias, it largely focuses on final outputs of models, with limited understanding…
arxiv.org1 month agoView details
FreeBalance: Pre-Routing Online Moe Load Balancing via Residual Workload Prediction
arXiv:2608.14205v1 Announce Type: new Abstract: Load imbalance poses a major bottleneck to the efficiency of expert parallelism in distributed inference of Mixture-of-Experts (MoE) models. The most heavily loaded rank stalls global execution due to skewed routing distributions, directly increasing latency. While offli…
arxiv.org1 month agoView details
Think in Latent, Explain in Language: Self-Explainable Latent Reasoning
arXiv:2608.13570v1 Announce Type: new Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings. However, compressing reasoning into the latent space renders the…
arxiv.org1 month agoView details
APTER: Adaptive Post-Training with Expert-Grounded Rubrics
arXiv:2608.14212v1 Announce Type: new Abstract: As large language models enter professional domains, they must satisfy domain constraints, include critical evidence, and provide complete reasoning rather than merely produce fluent responses. Existing post-training methods often rely on holistic preferences or outcome-…
arxiv.org1 month agoView details
arXiv:2608.13854v1 Announce Type: new Abstract: Code translation must preserve executable behavior across many programming languages, yet neural code translation has largely focused on a few popular languages such as C++, Java, and Python. This leaves a niche, many-to-many setting where parallel supervision is sparse,…
arxiv.org1 month agoView details
Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion
arXiv:2608.13947v1 Announce Type: new Abstract: High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conventions of diverse creative formats. We propose an attribute-guided genre expansion framework…
arxiv.org1 month agoView details
The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning
arXiv:2608.14229v1 Announce Type: new Abstract: Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless of training-data frequency. We propose the AdaPop (Adaptive Popularity) method, which combi…
arxiv.org1 month agoView details
Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL
arXiv:2608.14312v1 Announce Type: new Abstract: Reinforcement learning (RL) for terminal agents needs executable training environments with reliable rewards and useful difficulty. Fixed recipes such as few-shot, Self-Instruct, and Evol-Instruct apply the same prompting policy to every seed, even when the current polic…
arxiv.org1 month agoView details
A Survey of Large Models in Sports
arXiv:2608.14377v1 Announce Type: new Abstract: Sports have witnessed growing global enthusiasm in recent years, serving as a vital force for physical health, cultural exchange, social connection, and economic growth. The rapid advancement of large models, particularly (multimodal) large language models (M)LLMs, has d…
arxiv.org1 month agoView details
VoiceChat-TTS: A Low-Latency Continuous Speech Synthesis Model for Interactive Agents
arXiv:2608.13831v1 Announce Type: cross Abstract: Spoken dialogue is a natural form of human--computer interaction, yet most speech language models remain limited to turn-based operation and lack real-time adaptability, such as user barge-in. Recent duplex speech-to-speech and speech-to-text models reduce latency by r…
arxiv.org1 month agoView details
Agentic Transaction: Towards ACID-Compliant Agent Systems
arXiv:2608.13900v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents increasingly operate over persistent environments a…
arxiv.org1 month agoView details
QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction
arXiv:2608.13966v1 Announce Type: cross Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aware training (QAT) essential for preserving model quality. However, QAT computes the loss and surrogate gradients using…
arxiv.org1 month agoView details
SemPlan: Benchmarking Structured Semantic Planning for LLM-Based Queries over Enterprise Data
arXiv:2608.13612v1 Announce Type: new Abstract: Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism. SemPlan Benchmark evaluates this architectural design space with a deter…
arxiv.org1 month agoView details
arXiv:2608.13617v1 Announce Type: new Abstract: Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either paradigm alone. We present an expert-guided pipeline that constrains a large language model strictly to semantic normaliza…
arxiv.org1 month agoView details
BM25-Augmented Many-Shot Translation for Low-Resource North-Eastern Indian Languages
arXiv:2608.13722v1 Announce Type: new Abstract: This paper describes the University of Florida Gators submission to the WMT26 Low-Resource Indic Language Translation shared task. We adapt the retrieval-augmented many-shot translation pipeline from our AmericasNLP 2026 system to translate between English and eleven Nor…
arxiv.org1 month agoView details
AI Evaluation Should Work With Humans
arXiv:2608.13577v1 Announce Type: new Abstract: This position paper argues that the dominant paradigm of AI evaluation (which focuses on superhuman autonomous performance and so implicitly targets the goal of replacing humans) is guiding AI development in the wrong direction. Instead, the AI community should pivot to…
arxiv.org1 month agoView details
Evaluating Agentic Learning Harness Capabilities Without Labels via the Scaling Hypothesis
arXiv:2608.13608v1 Announce Type: new Abstract: Agentic "Continual Learning Harnesses", systems that pair an LLM with retrieval or memory to improve from feedback without retraining, have shown growing value in cybersecurity. But their value is conventionally measured by gains against labeled benchmarks, an approach t…
arxiv.org1 month agoView details
Algorithm Design and Physician Liability
arXiv:2608.13618v1 Announce Type: new Abstract: A single clinical algorithm can deliver unequal accuracy across patient groups, and concern about such disparity has grown as artificial intelligence (AI) spreads through clinical decision-making. In response, a liability rule introduced in the United States holds health…
arxiv.org1 month agoView details
Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy
arXiv:2608.13990v1 Announce Type: new Abstract: Driven by the attention economy, short-video Recommender Systems (RSs) are primarily optimized to maximize user engagement by promoting videos that capture attention within seconds. These systems inherently favor shallow-content videos that are effective at attracting im…
arxiv.org1 month agoView details
Repair, Not Improvement: Decomposing Constrained Decoding in Tool-Call Abstention
arXiv:2608.13959v1 Announce Type: new Abstract: Function calling is what the recent accounting of constrained generation explicitly sets aside: it finds the decoder's contribution small for format constraints, then warns in its Section 7 against extrapolating where a constraint encodes a correctness requirement, and n…
arxiv.org1 month agoView details
arXiv:2608.13940v1 Announce Type: new Abstract: AI research agents (AIRA) can now propose, implement, and evaluate their own machine learning experiments, but progress on frontier tasks is throttled by cost: a candidate solution can be written in minutes, whereas evaluating it can take hours to days of GPU time. An ag…
arxiv.org1 month agoView details
Active Perception for Embodied Disambiguation
arXiv:2608.13605v1 Announce Type: new Abstract: Natural language provides robots with a flexible task interface, but target ambiguity in embodied environments arises not only from user intent; it can also result from missing taskrelevant physical evidence in the current observation. Existing interactive disambiguation…
arxiv.org1 month agoView details
Second Thought: Reasoning in Parallel as LLM Agents Act and Observe
arXiv:2608.13667v1 Announce Type: new Abstract: LLM agents in the ReAct paradigm alternate between reasoning, acting, and observing, but deliberate reasoning is confined to the Thought phase: while the agent serializes an action and waits for the environment, its reasoning is frozen. We identify this recurring interva…
arxiv.org1 month agoView details
arXiv:2608.13604v1 Announce Type: cross Abstract: Detection of misunderstanding is an urgent problem to solve because communication has moved away from real-time, in-person interaction and is increasingly handled by AI-mediated channels. This shift cuts communicators off from the resources repair depends on faster tha…
arxiv.org1 month agoView details
MobileMem: Learning from a Year of Mobile Experiences
arXiv:2608.13606v2 Announce Type: cross Abstract: The next generation of AI agents is increasingly moving beyond systems that answer isolated questions toward persistent personal assistants that can understand, remember, and continuously learn from users' experiences. Such assistants require long-term memory to accumu…
arxiv.org1 month agoView details
HELIX: Model-Harness Co-evolution for Recursive Self-Improvement
arXiv:2608.13951v1 Announce Type: new Abstract: Scaling agent capability has largely focused on improving the model, yet an interactive agent acts through a runtime harness that mediates context, tools, control flow, and stopping. The harness shapes both what a model can accomplish and the trajectories from which it l…
arxiv.org1 month agoView details
How Much Do Legal RAG Systems Still Hallucinate?
arXiv:2608.14210v1 Announce Type: new Abstract: Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious consequences. To better understand this problem, we conduct a fine-grained analysis of hallucination behavior in eight le…
arxiv.org1 month agoView details
ASSERT: A Measurement Pipeline for GenAI Audits
arXiv:2608.13840v1 Announce Type: new Abstract: Audits of generative AI (GenAI) systems often summarize behavior as a reported rate: how often the audited system complies with policy. Researchers and stakeholders use that rate to compare systems, track regressions, and gate deployment. A reported rate reflects both th…
arxiv.org1 month agoView details
Agentao: A Governed Local-First Runtime for Tool-Using LLM Agents
arXiv:2608.13574v1 Announce Type: new Abstract: LLM agents increasingly operate as execution systems that invoke tools, modify local state, use persistent memory, and interact with external protocols. These capabilities make agents useful, but they also introduce risks related to over-privileged actions, weak auditabi…
arxiv.org1 month agoView details
Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit
arXiv:2608.13674v1 Announce Type: cross Abstract: I document an ideologically asymmetric break in the pre-existing diversification trend of political discourse, emerging around late 2022, using 6 million Reddit comments from two cross-partisan forums, 2019-2025. Conservative users experienced an interruption of their…
arxiv.org1 month agoView details
SDO: Subspace Deconflicting Operator for Multi-Adapter Composition
arXiv:2608.13820v1 Announce Type: new Abstract: Composing independently trained adapters within a shared diffusion backbone provides a modular approach to multi-character generation, but naive joint deployment often causes identity mixing, cross-character attribute leakage, and unstable scene composition. We study thi…
arxiv.org1 month agoView details
A Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation
arXiv:2608.14329v1 Announce Type: cross Abstract: Principle-based regulation, with evaluative standards such as "fair, clear, and not misleading" or "deliver good outcomes", cannot be reduced to binary predicates, and LLM-as-judge is increasingly used as the substitute. Our position is that any such judge must be eval…
arxiv.org1 month agoView details
arXiv:2608.14152v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) requires concise core opinions from large information streams. Off-the-shelf models struggle to filter noise from these…
arxiv.org1 month agoView details
Coverage Aware Active Evaluation for Failure Discovery with Paired Systems
arXiv:2608.13719v1 Announce Type: new Abstract: Autonomous systems can fail in rare and heterogeneous ways, making real-world failure discovery difficult under limited testing budgets. Although cheaper proxies such as simulators, lower-fidelity systems, or related policies can be sampled extensively to find failures,…
arxiv.org1 month agoView details
Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors
arXiv:2608.13591v1 Announce Type: cross Abstract: High-confidence errors in large language models are often treated as evidence of fragile internal inference. We study a different possibility: stable miscalibration, where a confident wrong answer remains locally stable under small perturbations. We combine two diagnos…
arxiv.org1 month agoView details
Reward Machines for Signal Temporal Logic
arXiv:2608.13625v1 Announce Type: new Abstract: Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction. Control synthesis from STL specifications is of interest since manual controlle…
arxiv.org1 month agoView details
MemoryLake on MemoryArena: A Matched Study of Agent Memory Backends
arXiv:2608.13883v1 Announce Type: new Abstract: Most agent-memory benchmarks test post-hoc recall, whereas MemoryArena evaluates whether memory supports interdependent, multi-session task completion. We compare MemoryLake, a structured multi-track memory backend, with Mem0, text-embedding-3-small vector RAG, and a lon…
arxiv.org1 month agoView details
Demystifying Agent Skills: Why They Work-Until They Don't
arXiv:2608.14036v1 Announce Type: new Abstract: Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental questio…
arxiv.org1 month agoView details
FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC
arXiv:2608.13774v1 Announce Type: new Abstract: Markov chain Monte Carlo (MCMC) requires only the ability to evaluate the likelihood, making it a common technique for inference in complex models. However, it can have a slow mixing rate, requiring the generation of many samples to obtain good estimates and an overall h…
arxiv.org1 month agoView details
Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation
arXiv:2608.13624v1 Announce Type: new Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness across demographic subgroups. Fairness evaluation in spoken-input settings is challenging d…
arxiv.org1 month agoView details
When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict
arXiv:2608.13921v1 Announce Type: new Abstract: LLM agents increasingly maintain personal memory across sessions, but it can conflict. Preferences depend on context, behavior evolves, and sources can conflict. When a query lacks context, time, or source authority to interpret conflict, treating one memory as definitiv…
arxiv.org1 month agoView details
Exploring ESC Winners with Nested Diagrams
arXiv:2608.13630v1 Announce Type: new Abstract: We present ConceptFlow, a scikit-learn-compatible Python library for Formal Concept Analysis that constructs and renders nested line diagrams from many-valued formal contexts. Given a many-valued context and a partition of its attributes into conceptual scales, ConceptFl…
arxiv.org1 month agoView details
Learning to Assemble Novel Structures with Unfamiliar Parts under Semantic Constraints
arXiv:2608.13684v1 Announce Type: new Abstract: This paper describes a neurosymbolic architecture for learning to assemble novel structures using evidence from embodied conversations and task demonstrations. We focus on scenarios where an agent encounters, after deployment, semantic constraints on structures--in other…
arxiv.org1 month agoView details
Measuring Cross-Task Behavioral Consistency in Language Model Agents
arXiv:2608.13598v1 Announce Type: new Abstract: Agent evaluation relies almost entirely on outcome metrics such as success rate, which capture whether an agent succeeds but not how consistently it behaves. We argue that behavioral consistency across tasks is a distinct and measurable property, and we introduce the Beh…
arxiv.org1 month agoView details
ARC: Fair Relative Advantage Comparison in Open-Ended Real-World Interaction
arXiv:2608.13622v1 Announce Type: cross Abstract: Open-ended real-world interaction admits multiple valid behaviors: an agent may answer directly, ask for clarification, provide progress updates, or confirm before acting. This flexibility breaks a core assumption behind group-based RL: rollouts compared within a group…
arxiv.org1 month agoView details
Ontology-Grounded Project Memory for Coding Agents
arXiv:2608.13662v1 Announce Type: new Abstract: Coding agents have become the primary means of generating new code in many software projects, and the resulting velocity of changes makes keeping track of the reasons behind those changes challenging. This paper introduces MOOSEDev, a system designed to give coding agent…
arxiv.org1 month agoView details
Implementing Computational Law in Wolfram Language for the Governance of Artificial Intelligence
arXiv:2608.13958v1 Announce Type: new Abstract: How do we govern AI systems whose reasoning we cannot fully inspect? Governance does not require understanding a system's reasoning. It requires stating what the system is obliged, permitted, and forbidden to do, and checking whether it complied. I present an implementat…
arxiv.org1 month agoView details
arXiv:2608.14397v1 Announce Type: cross Abstract: Zahavy [2026] argues that Large Language Models, despite their capabilities in induction and deduction, cannot perform the abductive "Jump" that produced Einstein's equivalence principle, and attributes this limitation to the absence of embodied simulation. Zheng-Xin […
arxiv.org1 month agoView details
Modular Cognitive Architecture Emerges in Large Language Models
arXiv:2608.13567v1 Announce Type: cross Abstract: The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning about the physical world. Is this modular organization a fundamental principle of how intel…
arxiv.org1 month agoView details
Leading-Silence Augmentation and Multi-Stage Synthetic Supervision for the Second MLC-SLM Challenge
arXiv:2608.14150v1 Announce Type: new Abstract: The second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge evaluates two tasks over complete, unsegmented multilingual conversations: speaker diarization and recognition (Task 1) and conversational speech understanding (Task 2). Neither task provide…
arxiv.org1 month agoView details
arXiv:2608.13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations. Machine learning models trained on such sparse data cannot generalize to unmonitored locations and must be retrained whenever the sensor infr…
arxiv.org1 month agoView details
AnchorBench: A Multi-Pathway Benchmark for the Anchoring Effect in LLMs
arXiv:2608.14320v1 Announce Type: cross Abstract: The anchoring effect is a cognitive bias in which an initial reference value shifts a later judgment toward itself. This effect is well established in human judgment and decision-making, and recent work suggests that large language models (LLMs) exhibit similar behavio…
arxiv.org1 month agoView details
Capacity-Dependent Effects of Data Selection for Reasoning
arXiv:2608.13721v1 Announce Type: cross Abstract: In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student's current distribution. Recent likelihood-based response selection methods suggest that responses closer to the student distrib…
arxiv.org1 month agoView details
arXiv:2608.14465v1 Announce Type: new Abstract: A frozen language model on reasoning tasks has two coupled weaknesses: it under-uses evidence its own residual stream already encodes, and it fails to detect when the input is insufficient to answer, so it confabulates. This paper consolidates two research lines that add…
arxiv.org1 month agoView details
Jais 2: A Family of Arabic-Centric Open Large Language Models
arXiv:2608.13580v1 Announce Type: new Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. T…
arxiv.org1 month agoView details
Benchmarking data-driven material models on the classic Treloar dataset
arXiv:2608.14063v1 Announce Type: new Abstract: Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling paradigms. But with a growing number of machine-learning-based approaches available, how do t…
arxiv.org1 month agoView details
A Calibrated Test of Internal Action Maps: State Signals Without Global Affine Closure
arXiv:2608.13626v1 Announce Type: cross Abstract: A hidden state signal can be decodable or causally usable without supporting a reusable action map. We test whether action maps fitted without a source reach its natural post-action activation and compose. We organize the tests as an evidence lattice and validate the g…
arxiv.org1 month agoView details
Scaling Domain Data Repetition in LLM Pretraining
arXiv:2608.14071v1 Announce Type: new Abstract: As large language models scale, their training-token budgets must also increase to maintain an appropriate tokens-per-parameter ratio (\(\mathrm{TPP}\)). However, high-quality domain data is much harder to scale than general web data. As model size and the training-token…
arxiv.org1 month agoView details
A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing
arXiv:2608.13573v1 Announce Type: new Abstract: Large Language Model (LLM) serving has become a critical cloud workload, and realistic traces are essential for motivating and benchmarking serving systems. However, existing LLM serving workload studies remain limited in scale and scope. They often observe short time pe…
arxiv.org1 month agoView details
arXiv:2608.14021v1 Announce Type: new Abstract: Transformer-based sequential recommenders with causal self-attention often rely heavily on the most recent interaction at inference time, but how this behavior is structurally expressed in the representation used for prediction remains unclear. We combine prediction-time…
arxiv.org1 month agoView details
Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models
arXiv:2608.14252v1 Announce Type: cross Abstract: Recent work suggests that some large language model representations have content or reference. Grounding can secure either without supplying live routes for correction. This paper asks what follows from that gap. An output is answerable when discrepancies can affect wh…
arxiv.org1 month agoView details
Seeing Red, Thinking Bad: Color Bias in Vision Language Models
arXiv:2608.14286v1 Announce Type: cross Abstract: Vision language models (VLMs) are increasingly used in industrial decision-making systems, such as recruitment support and recommendation. This motivates careful analysis of how VLMs process visual and textual information. In this work, we study how VLMs interpret text…
arxiv.org1 month agoView details
arXiv:2608.14221v1 Announce Type: cross Abstract: Autoformalization is commonly framed as translating natural-language mathematical statements into machine-verifiable formal languages such as Lean 4. However, faithful formalization requires more than translation. Models must map mathematical concepts to the complex hi…
arxiv.org1 month agoView details
Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification
arXiv:2608.13866v1 Announce Type: cross Abstract: Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some fall in correct class regions of the embedding space while others land in peripheral or cross-class zones. We propose…
arxiv.org1 month agoView details
S2Dialog: Multimodal Dialogue Retrieval with Semantic and Acoustic-Style Modeling
arXiv:2608.14029v1 Announce Type: new Abstract: Multimodal dialogue retrieval aims to retrieve dialogues from multimodal dialogue banks that are similar to a target dialogue in terms of both textual semantics and acoustic conversational styles. Such dialogue-level retrieval is crucial for many dialogue-related tasks,…
arxiv.org1 month agoView details
arXiv:2608.13621v1 Announce Type: new Abstract: A hidden Markov model (HMM) combines three roles: inference of a hidden-state belief from observations, propagation through a Markov transition, and emission back to observation space. We show that full, time-indexed Predictive Information Bottleneck VJEPA (PIB-VJEPA) ex…
arxiv.org1 month agoView details
The conditional superiority of fast silicon sampling
arXiv:2608.14079v1 Announce Type: new Abstract: Silicon sampling can produce surprisingly good population estimates at times. Does doing it fast attenuate such fidelity? In this study, we extend and assess ongoing work in silicon sampling by comparing the algorithmic fidelity of "fast" and "slow" modes of silicon samp…
arxiv.org1 month agoView details
Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact
arXiv:2608.13926v1 Announce Type: cross Abstract: Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters for deployment: a hallucinated column or a mis-aggregated total yields a fluent wrong answer, indistinguishable at th…
arxiv.org1 month agoView details
IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering
arXiv:2608.13588v1 Announce Type: new Abstract: Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy contexts, thereby undermining both efficiency and accuracy. While existing prompt compression m…
arxiv.org1 month agoView details
Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation
arXiv:2608.13712v1 Announce Type: cross Abstract: Deposition training requires attorneys to manage dynamic witness behavior, yet legal-AI evaluations largely focus on factual accuracy, reasoning, or response-level plausibility. We introduce WitnessSim, a deposition simulator driven by controllable legal personas. We u…
arxiv.org1 month agoView details
BCMT: Blockwise Causal Memory Transformer
arXiv:2608.13578v1 Announce Type: new Abstract: Transformer architectures rely on dense self-attention to model long-range dependencies, but this mechanism exhibits quadratic complexity with respect to sequence length. We introduce BCMT (Blockwise Causal Memory Transformer), an architecture for long-context language m…
arxiv.org1 month agoView details
CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing
arXiv:2608.13925v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) accelerate language generation by predicting multiple masks in a single forward pass. However, existing dLLMs can suffer from unreliable predictions in early denoising stages under aggressive parallelism strategies, leading to er…
arxiv.org1 month agoView details
No Universal Signal Predicts Sample-Level LLM Regression under Version Updates
arXiv:2608.13607v1 Announce Type: cross Abstract: Frontier LLMs are updated frequently and typically outperform their predecessors in aggregate. But aggregate gains say little about individual samples: an update can still cause sample-level regression, where a response correct under the old model becomes incorrect und…
arxiv.org1 month agoView details
KV Cache Compression Through the Lens of Transform Coding
arXiv:2608.14191v1 Announce Type: cross Abstract: The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods address this bottleneck by representing the KV cache uniformly with lower-precision data types and designing quantizat…
arxiv.org1 month agoView details
Inducing Reward-Free Judging Rubrics that Reduce Over-Crediting in Agent Evaluation
arXiv:2608.13564v1 Announce Type: new Abstract: Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable environment reward, is expensive, slow, or unavailable at deployment time. Such a judge is a reward-free proxy whose val…
arxiv.org1 month agoView details
Models
View all models →huihui-ai/Huihui-Qwen3.8-27B-abliterated
image-text-to-text · transformers · safetensors · qwen3_5
huggingface.co1 month ago237 ptsView details
Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF
image-text-to-text · gguf · qwen3.8 · qwen
huggingface.co1 month ago203 ptsView details
image-text-to-text · gguf · llama.cpp · qwen3.8
huggingface.co1 month ago132 ptsView details
DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1
image-text-to-text · transformers · safetensors · qwen3_5
huggingface.co1 month ago127 ptsView details
barozp/Qwen3.8-27B-Opus-Distill-GGUF
image-text-to-text · llama.cpp · gguf · qwen
huggingface.co1 month ago8 ptsView details
zerodigest/Qwen3.8-27B-YMQ-MTP-GGUF
text-generation · gguf · text-generation · quantizer
huggingface.co1 month ago6 ptsView details
malaiwah/Qwen3.8-27B-EXL3-K5K6-hydrated
image-text-to-text · vllm · safetensors · qwen3_5
huggingface.co1 month ago5 ptsView details
Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b
text-generation · safetensors · gguf · lfm2
huggingface.co1 month ago3 ptsView details
Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2
text-generation · safetensors · gguf · gemma3
huggingface.co1 month ago3 ptsView details
neko-legends/DeepSeek-V4-Flash-0731-Abliterated-NVFP4
text-generation · deepseek-v4 · nvfp4 · fp8
huggingface.co1 month ago3 ptsView details
Pilcothink/Qwen3.8-27B-MixedInt4-AutoRound
image-text-to-text · transformers · safetensors · qwen3_5
huggingface.co1 month ago2 ptsView details
Davidmg0815/Qwen3.8-27B-MTP-GGUF
image-text-to-text · llama.cpp · gguf · mtp
huggingface.co1 month ago1 ptsView details
neureps/warmly-qwen35-08b-enko-gguf
image-text-to-text · gguf · llama.cpp · korean
huggingface.co1 month agoView details
LuRr/babylm-chinese-deberta-v2-14m-CL
fill-mask · transformers · safetensors · deberta-v2
huggingface.co1 month agoView details
joannetai520/16_bit_model_trymoredata1
text-generation · transformers · safetensors · llama
huggingface.co1 month agoView details
text-generation · transformers · safetensors · llama
huggingface.co1 month agoView details
Open source
View all open source →Open-source AI coworkers that each get a computer of their own: a browser, files and tools, with every action decided before it happens and recorded after. Bring any AG-UI agent.
github.com1 month ago37 ptsView details
Fine-tune and post-train LLMs with one command—no SSH, no config hell.
github.com1 month ago22 ptsView details
<details open> model : remove some ggml_concat (#27176) Co-authored-by: Xuan Son Nguyen <son@huggingface.co> </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10453/llama-b10453-b…
github.com1 month agoView details
<details open> llama : check LoRA tensor data is within file bounds (#27056) * llama : check LoRA tensor data is within file bounds * Update src/llama-adapter.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigb…
github.com1 month agoView details