Search: “llm”

Latent-MOPD: Latent Multi-Teacher On-Policy Distillation

arXiv:2610.02381v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through t

Local Ai

Law And Order: Tax Law Autoformalization

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DGX agent

arXiv:2610.02792v1 Announce Type: new Abstract: Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations rem

local-aiarxiv-cs-ai

5 Oct 2026

Agents

Optimal Planning in a Dynamic World

arXiv:2610.03312v1 Announce Type: new Abstract: Background: We address the problem of planning when the set of feasible states or actions changes over time. For example, in the problem of path plannin

agentsarxiv-cs-ai

Page-EntroKV: Hardware-Aligned, Entropy-Weighted KV-Cache Eviction under Grouped-Query Attention

arXiv:2610.03135v1 Announce Type: new Abstract: Serving long-context autoregressive language models is constrained by the key-value (KV) cache. Most dynamic eviction methods score token importance per

researcharxiv-cs-lg

Planning to Learn

arXiv:2610.03667v1 Announce Type: new Abstract: Policy-gradient methods are central to modern reinforcement learning, including LLM post-training. When they struggle, the usual suspects are exploratio

Positive-Unlabeled Learning for Agent Safety False Alarm Auditing

arXiv:2610.02925v1 Announce Type: new Abstract: Safety monitors help safeguard language-model agents interacting with external tools and environments, but conservative monitoring can generate many fal

Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection

arXiv:2610.03082v1 Announce Type: new Abstract: Bridges contribute significantly to transportation connectivity and urban development. Therefore, reliable bridge monitoring is crucial for protecting p

SoftGene: Protein Language Model-Enhanced Soft Prompting for Interpretable Gene Set Annotation

arXiv:2610.03029v1 Announce Type: new Abstract: Gene set analysis is a cornerstone of functional genomics, yet it remains labor-intensive and heavily dependent on manual curation and expert biological

Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks

arXiv:2610.03585v1 Announce Type: cross Abstract: Security benchmarks for LLM-based agents often report the attack success rate (ASR) as a measure of model robustness and use these scores to compare d

To Jev or Not? Evaluating the Accuracy and Efficiency of Structured Decision Models for Hate-Speech Moderation

arXiv:2610.03324v1 Announce Type: new Abstract: The scale of online content makes hate-speech moderation challenging, while Large Language Models (LLMs) enable harmful material to be produced and adap

researcharxiv-cs-cl

Local Ai

Writerslogic at PAN 2026: Process over Content for Robust Detection under Domain Shift

arXiv:2610.03565v1 Announce Type: new Abstract: We describe the Writerslogic systems for three PAN at CLEF 2026 shared tasks (Reasoning Trajectory Detection, Voight-Kampff Generative AI Detection, and

local-aiarxiv-cs-cl

Agent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard

arXiv:2610.00651v1 Announce Type: new Abstract: Agent evaluations are increasingly used to compare LLMs and inform deployment decisions, yet ranks can reflect not only the model but also the effects o

Beyond Linear Concepts: Discovering and Aligning Non-Linear Concept Manifolds in Large Language Models

arXiv:2610.01821v1 Announce Type: new Abstract: Understanding information processing in large language models (LLMs) requires dissecting the geometric organization of their internal token representati

Constitutional Value Potentials: reading and steering internal priority margins in language models

arXiv:2606.15420v2 Announce Type: replace-cross Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dash

HydroJEV: A one-second, training-free screen for cyber-attack and fault attribution in water distribution networks

arXiv:2610.02048v1 Announce Type: new Abstract: When a SCADA alarm is raised in a water distribution network, operators must decide quickly whether it reflects a cyberattack, a physical fault, a norma

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

arXiv:2610.00437v1 Announce Type: new Abstract: LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models

JoinGR: Learning to Traverse Join Graphs for Table Retrieval

arXiv:2610.01064v1 Announce Type: cross Abstract: Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but

KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards

arXiv:2610.02206v1 Announce Type: cross Abstract: LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations. However, existi

Local Ai

Managing Context and Communication in Distributed Agentic UAV Swarms

arXiv:2610.01569v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV) swarms increasingly rely on language-model agents to provide adaptive mission-level reasoning in uncertain environments.

local-aiarxiv-cs-ai

Measuring Iterative Temporal Reasoning with Time Puzzles

arXiv:2601.07148v4 Announce Type: replace-cross Abstract: Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing benchm

Applications

Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)

arXiv:2610.01533v1 Announce Type: new Abstract: Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a qua

applicationsarxiv-cs-ai

OmniSeek: Native Tool Integration for Multi-turn Audio-Visual Reasoning

arXiv:2610.02181v1 Announce Type: new Abstract: We present OmniSeek, an agentic framework that transforms an Omni Large Language Model (Omni-LLM) into an active, multi-turn reasoning agent with native

safetyarxiv-cs-cv

On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidence

arXiv:2610.00007v1 Announce Type: cross Abstract: Named-entity recognition (NER) is increasingly wanted on-device (no API, low latency, data kept local). The practitioner's question is not the leaderb

On Language Drift during RLVR Post-Training

arXiv:2610.02015v1 Announce Type: cross Abstract: Recent advances in LLM reasoning models---driven primarily by the paradigm of post-training via reinforcement learning with verifiable reward (RLVR)--

Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals

arXiv:2610.01548v1 Announce Type: new Abstract: As the use of large language models (LLMs) expands, post-training has become increasingly important for adapting them to downstream tasks. However, obta

Refusal Localizes, the Damage Relocates: Safety Layers Under Few-Sample Fine-Tuning

arXiv:2610.00320v1 Announce Type: cross Abstract: Fine-tuning adapts aligned large language models (LLMs) to downstream tasks, but a few dozen harmful examples can remove their refusal of harmful requ

Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)

arXiv:2610.02092v1 Announce Type: cross Abstract: Data selection is critical for training large language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

arXiv:2610.00838v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error c

The Delegation Danger Band: Why Mid-Capability Sub-Agents Over-Trust Inherited Stale State

arXiv:2610.00041v1 Announce Type: cross Abstract: Agent frameworks increasingly delegate work by forking sub-agents; a common default makes the child inherit the parent's full working context. We meas

TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design

arXiv:2610.01887v1 Announce Type: cross Abstract: Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastru

When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design

arXiv:2610.01188v1 Announce Type: new Abstract: Exercise-specific joint selection can improve skeleton-based correctness classification, but what does that gain establish? We audit 1,057 repetitions f

ActionGuard: Tool Call Authorization under Poisoned Skills

arXiv:2609.39450v1 Announce Type: cross Abstract: LLM-based agents extend their capabilities through third-party skills that provide task-specific instructions, scripts, and tool-use procedures. Howev

Always-On Experimentation

arXiv:2609.38695v1 Announce Type: cross Abstract: Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be concei

An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices

arXiv:2601.02641v2 Announce Type: replace Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as t

Backward-State Policy Is Part of the Learning Algorithm

arXiv:2609.39813v1 Announce Type: new Abstract: Low-precision training rounds tensors that the backward pass reads again, often for several gradients; each use can read the forward's rounded value, th

Beyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent

arXiv:2609.38604v1 Announce Type: cross Abstract: Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume

Hardware

Bongard: Training Machine Intuition

arXiv:2609.39111v1 Announce Type: new Abstract: Human intelligence relies heavily on learned intuition: recognising patterns and judging situations without explicitly unfolding every intermediate step

hardwarearxiv-cs-cl

Tutorials

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This r

tutorialsarxiv-cs-ai

Agents

Coding Agents for Coding Theory

arXiv:2609.39081v1 Announce Type: cross Abstract: We spent five weeks using an LLM coding agent on open problems in coding theory: finding large sets of four-letter words, such as DNA barcodes, that s

agentsarxiv-cs-ai

Agents

ConflictGuide: AutoResearch Improves When Competing Behaviors Are Made Visible

arXiv:2609.39933v1 Announce Type: new Abstract: When designing machine learning models, desirable properties are often in tension: improving one behavior can impair another, so task progress can depen

agentsarxiv-cs-ai

Context-Aware Classification and Grading of Sensitive Information in Online Conversational Health Data

arXiv:2601.09717v2 Announce Type: replace-cross Abstract: Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but als

EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making

arXiv:2609.38334v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model

safetyarxiv-cs-cl

How Much Human Label Variation Does Formal Semantic Structure Explain?: Group-Level Effects and Item-Level Ceilings in NLI

arXiv:2607.15870v2 Announce Type: replace Abstract: Human label variation in natural language inference is increasingly treated as signal rather than noise, but how much of it formal semantic structur

researcharxiv-cs-cl

Marginal Response Surface Elicitation for Zero-Label Tabular Learning

model-releasesarxiv-cs-cl

arXiv:2609.39639v1 Announce Type: new Abstract: Tabular learning uses structured data to predict target outcomes. Traditionally, this process has relied on labeled data. However, large language models

MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models

arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantize

researcharxiv-cs-lg

Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents

arXiv:2609.39982v1 Announce Type: cross Abstract: Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor c

On the (In)effectiveness of AMR Augmentation for Large Language Models

arXiv:2609.40121v1 Announce Type: cross Abstract: While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit---or lack thereof---of AMR augm

Pretext: Defeating Malicious Skill Detection Frameworks for AI Agents

arXiv:2609.39607v1 Announce Type: cross Abstract: Skills extend an agent's capabilities by injecting instructions and information into the context, and are widely used by agents such as OpenClaw and C