Search: “llm”

Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception

arXiv:2610.12445v1 Announce Type: cross Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box deception d

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Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

DGX agent

arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical

safetyarxiv-cs-ai

9 Oct 2026

Harness Evolution Hits a Ceiling: When Weight Training Should Begin

arXiv:2610.11655v1 Announce Type: new Abstract: Improving a long-horizon LLM agent means evolving the harness around a frozen model or training its weights. We let a self-evolving harness make the sys

Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models

arXiv:2610.12303v1 Announce Type: new Abstract: Declarative logic programs offer a powerful and interpretable abstraction for encoding relational structure and neurosymbolic reasoning, by expressing d

Neural Architecture Discovery via Autonomous Evolution

arXiv:2507.18074v2 Announce Type: replace Abstract: Recent progress in LLM agents has advanced the prospect of autonomous research. Yet whether AI can complete difficult long-horizon tasks, especially

On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents

arXiv:2610.10833v1 Announce Type: new Abstract: We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using avai

RaReCache: Bridging the Gap in Cross-Model KV Cache Reuse via Rank disagreement-based Selective Recomputation

arXiv:2610.11358v1 Announce Type: new Abstract: Cross-model KV-cache reuse remains a key challenge in modern LLM serving. Coding agents and multi-model systems increasingly route a shared context acro

Safe Actions Alone Do Not Ensure Safe Agents: Identifying Unfulfilled Obligations with Guard Models

arXiv:2610.11773v1 Announce Type: new Abstract: Guard models are increasingly used to safeguard LLM-based agents, primarily by identifying actions that agents are forbidden to perform. However, identi

Speedbumps: Rejection Attacks on Speculative Decoding

arXiv:2610.10929v1 Announce Type: cross Abstract: Speculative decoding is a popular technique for increasing the speed and reducing the costs of large language model (LLM) inference by verifying multi

StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents

arXiv:2610.10942v1 Announce Type: new Abstract: Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic ben

SWE-Journey: Towards More Realistic Evaluation of Coding Assistants through Long-Horizon, Multi-Turn Interaction

arXiv:2610.11559v1 Announce Type: cross Abstract: Coding assistants such as Claude Code and Codex have become a major application of LLM agents, yet existing benchmarks remain far from real-world use,

TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution

arXiv:2610.10619v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all exist

Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants

arXiv:2610.12281v1 Announce Type: cross Abstract: Over 90% of disease-associated variants from genome-wide association studies fall in noncoding regulatory regions, yet their functional interpretation

VFold: Symmetry-Aware Cross-Layer Value Cache Compression

arXiv:2610.12338v1 Announce Type: new Abstract: While caching key-value (KV) states accelerates Large Language Model (LLM) decoding, this cache can dominate memory usage at long context lengths. One s

When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation

arXiv:2610.12061v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities on complex tasks. They typically perform reasoning before each action thro

safetyarxiv-cs-ai

Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution

arXiv:2610.12367v1 Announce Type: new Abstract: Skills, reusable procedural guidance added at inference, can substantially improve LLM downstream performance (Li et al., 2026). Prior work retrieves sk

Workerville: Towards an Organizational Behavior Account of Agent Safety

arXiv:2610.11561v1 Announce Type: new Abstract: LLM-based agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long

Emo-Jev: Probabilistic Reasoning for Emotion Classification with Jev

arXiv:2610.08829v1 Announce Type: new Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than

Local Ai

Few Bits, One Law: Toward W2A4KV2

arXiv:2610.09202v1 Announce Type: cross Abstract: Extreme low-bit LLM compression is most challenging when weights, activations, and KV caches are quantized together: their distributions differ, and q

local-aiarxiv-cs-cl

Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness

arXiv:2610.09489v1 Announce Type: new Abstract: Many annotation projects begin before experts have a stable guideline or enough labels to train a task-specific model. We present Goldsmith, an agentic

agentsarxiv-cs-cl

Hidden in Plain Sight: Benchmarking Agent Safety Against Decomposition Attacks with DECOMPBENCH

model-releasesarxiv-cs-lg

arXiv:2606.13994v2 Announce Type: replace-cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world.

KVFetch: Temporal Prefetching for the Missing Half of KV Cache Compression

arXiv:2610.08811v1 Announce Type: new Abstract: As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference. Existing met

researcharxiv-cs-lg

Large-scale Repository Engineering via Agent-Native Reusable Code Primitives

arXiv:2610.09079v1 Announce Type: cross Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete re

LRCC: Generalizing Low-Rank Compression with Conditional Computation

arXiv:2610.08858v1 Announce Type: new Abstract: Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventio

Q-PACE: Dynamic Precision Allocation for Quantization-Aware Training

arXiv:2610.09183v1 Announce Type: new Abstract: Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final

researcharxiv-cs-lg

RELATE: An Evaluation Framework for measuring Relational Orientation of Large Language Models

arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world r

safetyarxiv-cs-cl

RunningTab: Direct Workspace Interaction with Environment-Side Tabs

arXiv:2610.10444v1 Announce Type: cross Abstract: Much knowledge work produces new deliverables from files a workspace already holds, and LLM agents are beginning to take such work over. Through direc

SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

arXiv:2602.08234v2 Announce Type: replace Abstract: Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past expe

safetyarxiv-cs-lg

AdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model

arXiv:2610.08773v1 Announce Type: cross Abstract: Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent

Agentic Design Space Exploration for Joint Hardware Configuration Selection and Mapping of AI Inference Workloads on Heterogeneous Edge SoCs

arXiv:2610.07191v1 Announce Type: cross Abstract: Modern edge Systems-on-Chip (SoCs) integrate heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, each with distinct performance and ene

Agentic RCA for Internet-Scale Services Using Constrained Creativity

arXiv:2610.08622v1 Announce Type: cross Abstract: System administrators of Internet-scale services need to resolve failure incidents to maintain reliability of such services. Ideally, we want a troubl

Beyond Corrected Memory: Execution Consistency in Multi-Agent Systems

arXiv:2610.08101v1 Announce Type: new Abstract: Shared memory coordinates agents' actions, but correct records do not establish that those actions satisfy task requirements. Memory governance and fail

Beyond the Leaderboard: Multi-Dimensional Evaluation of Dense and Mixture-of-Experts Models for Automated Program Repair

model-releasesarxiv-cs-lg

arXiv:2610.08173v1 Announce Type: cross Abstract: Automated Program Repair (APR) with language models is usually evaluated by whether a generated patch passes the test suite, which can hide difference

CLM-as-a-Judge: Evaluating an Open Contrastive Decision Model on Public Judge Benchmarks

arXiv:2610.07177v1 Announce Type: cross Abstract: An open contrastive decision model is near chance as a judge on the hard public benchmarks: Contrastive-LM/CLM-v0.1-8B scores between 0.351 (best- of-

Closing Ambient Clinical Documentation Gaps with Automated Provider Queries

arXiv:2610.07502v1 Announce Type: new Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate

CuratorMAS: Automating Dataset Curation via Multi-Agent Orchestration

arXiv:2610.07075v1 Announce Type: new Abstract: High-quality datasets are essential for reliable machine learning, but dataset curation remains costly and hard to generalize across domains. Existing m

Local Ai

Do I Need the Cloud? Uncertainty-Aware Step-Level Handoff for Small Language Model Agents

arXiv:2610.07816v1 Announce Type: new Abstract: Small language models (SLMs) are attractive as local agent controllers because they reduce remote inference, latency, and deployment footprint, yet stru

local-aiarxiv-cs-ai

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search

arXiv:2507.07426v4 Announce Type: replace Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their e

Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models

arXiv:2610.07620v1 Announce Type: new Abstract: Scientific law discovery requires selecting measurements and converting evidence into a governing equation. We evaluate an explore-then-commit protocol

FedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Models

arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially

safetyarxiv-cs-ai

Harness Engineering for Software Engineering via Modular Executable Dev-Primitives

arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, ex

HINTT Submission to the 2nd MLC-SLM Challenge: Comparing Cascaded and Unified Approaches to Diarization and ASR

arXiv:2610.08063v1 Announce Type: cross Abstract: This paper presents the HINTT system submitted to the 2nd Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM). We ad

Tutorials

How Learning Governs Unlearning across the Memorization-Generalization Spectrum

arXiv:2610.08577v1 Announce Type: cross Abstract: While unlearning seeks to negate undesired capabilities acquired through learning, little research has examined how the way models learn shapes their

tutorialsarxiv-cs-ai

Identifying Introspection From the Inside

arXiv:2610.07186v1 Announce Type: new Abstract: Large language models make claims about themselves that are both consequential and increasingly difficult to verify from behavior alone. How can we dist

Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution

arXiv:2610.07607v1 Announce Type: cross Abstract: Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide r

MASC: A Multi-Agent Self-Calibration Framework with Latent Construct Alignment for Consistent Client Role-Playing in Psychological Counseling

arXiv:2610.08250v1 Announce Type: new Abstract: Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation re

Tutorials

Mask-Guided KV Cache Eviction in Block Diffusion Language Models

arXiv:2610.06996v1 Announce Type: cross Abstract: Block diffusion language models keep a large key-value (KV) cache throughout generation and attend to it at every denoising step, limiting both memory

tutorialsarxiv-cs-ai

Massive Activation Gating Channel in Large Language Models

arXiv:2610.07661v1 Announce Type: new Abstract: Massive activations, a phenomenon in which a small number of hidden channels exhibit exceptionally large magnitudes, are pervasive in large language mod