Search: “llm”

ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

arXiv:2606.20235v2 Announce Type: replace-cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, i

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Research

Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena

DGX agent

arXiv:2610.19095v1 Announce Type: new Abstract: Chatbot Arena ranks language models with the Bradley-Terry model, which gives each model one score and so assumes Luce's choice axiom. Choice theory pre

researcharxiv-cs-cl

15 Oct 2026

SkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition

arXiv:2610.14966v1 Announce Type: new Abstract: Long-horizon agents must complete workflows in which later activities depend on earlier outputs and changes to shared state. A key challenge for evaluat

SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data

arXiv:2610.16412v1 Announce Type: new Abstract: As IT systems grow in complexity, automated agent-driven incident response becomes increasingly critical. A key capability for these agents is summarizi

Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents

arXiv:2610.15964v1 Announce Type: new Abstract: A self-evolving agent rewrites its own harness and must decide which rewrites to keep, so its improvement signal is the check a rewrite has to pass. Ver

Applications

SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges

arXiv:2610.19992v1 Announce Type: new Abstract: Law students are examined through open-text answers: case solutions, essays and memos that apply the law to a set of facts. Such answers are expensive t

applicationsarxiv-cs-cl

Suggested Is Not Superseded: A Pre-Registered Evaluation of a System-One Model as a Memory-Retirement Gate

arXiv:2610.15710v1 Announce Type: new Abstract: Long-term memory for assistants needs a gate that decides when a new utterance retires an old stored claim. Benchmark labels for this step mark a pair a

TextualRL: Textual Reinforcement Learning for Context Management

arXiv:2610.18216v1 Announce Type: cross Abstract: LLM agents can adapt to task feedback by revising the textual context that guides their behavior, without changing their model parameters. However, tu

The Invisible Leash: Why RLVR May or May Not Escape Its Origin

model-releasesarxiv-cs-cl

arXiv:2507.14843v5 Announce Type: replace-cross Abstract: Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However

Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision

arXiv:2610.16725v1 Announce Type: new Abstract: Improving an LLM agent requires knowing what to change when it fails. Similar failures can require different fixes, and resolving one problem can expose

VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness

arXiv:2610.19424v1 Announce Type: new Abstract: LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reason

agentsarxiv-cs-cv

When Is Enough Not Enough? Illusory Completion in Search Agents

arXiv:2602.07549v3 Announce Type: replace-cross Abstract: In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But

agentsarxiv-cs-cl

When Pleasing the User Compromises Care: AI Sycophancy in Caregiver-Mediated Healthcare

arXiv:2610.14360v1 Announce Type: new Abstract: Large language model (LLM) alignment often assumes that the user interacting with a model is also the primary beneficiary of its behavior. Yet in many c

When Skills Remain But Access Fails: Factorized Skill Memory for Continual Skill Internalization

arXiv:2610.15492v1 Announce Type: new Abstract: Skill internalization aims to convert externally specified procedural skills into persistent model capabilities, allowing language-model agents to execu

A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding

model-releasesarxiv-cs-cv

arXiv:2610.15666v1 Announce Type: new Abstract: Converting natural-language instructions into executable action plans conditioned on a scene image is a core capability for embodied human-robot interac

Auditing Failure Attribution: Label Predictability and Judge Behavior

arXiv:2610.17747v1 Announce Type: cross Abstract: Failure-attribution benchmarks ask an LLM judge which step or agent caused a failure. A high score should mean the judge checked the faulty step again

ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models

arXiv:2610.14427v1 Announce Type: cross Abstract: As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown. Existing detectors read the wa

researcharxiv-cs-lg

Dependency Reasoning for Aspect Sentiment Quadruple Prediction

arXiv:2610.14873v1 Announce Type: cross Abstract: Aspect Sentiment Quadruple Prediction (ASQP) is fundamentally a relational reasoning problem: identifying aspect-opinion-category-sentiment quadruples

researcharxiv-cs-lg

Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks

model-releasesarxiv-cs-ro

arXiv:2610.16238v1 Announce Type: new Abstract: LLM agents increasingly control robots using VLA policies, perception models, and procedural knowledge. These modules are difficult to reuse because the

EvolvAttack: Backdooring self Evolving VLM Agents

arXiv:2610.15244v1 Announce Type: new Abstract: Self-evolving mechanisms let vision-language model (VLM) agents continually adapt through evolving memory, but they also allow malicious behavior to eme

agentsarxiv-cs-lg

GameSmith: Scaling Zero-Sum Self-Play with Synthesised Games

arXiv:2610.13995v1 Announce Type: new Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to gener

Harness^2: Recursive Agent Harnessing for an Open World

arXiv:2610.14224v1 Announce Type: cross Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a p

LIFT: Likelihood-Based Intervention Faithfulness Training

arXiv:2509.13334v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on th

MFG: Multi-turn Feedback Guided Molecular Optimization

arXiv:2610.18851v1 Announce Type: cross Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple

researcharxiv-cs-lg

MultiAlloc: Budget-Aware Precision Allocation for KV Cache Quantization

arXiv:2610.18642v1 Announce Type: new Abstract: The Key-Value (KV) cache constitutes a dominant source of memory consumption during large language model (LLM) inference, particularly in long-context a

OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains

arXiv:2610.17143v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms

ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning

arXiv:2610.18683v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (

agentsarxiv-cs-lg

Representation Drift in Continual Learning: LoRA versus N-gram Memory

arXiv:2610.14921v1 Announce Type: new Abstract: A language model that keeps learning online must not slowly erode its own base capabilities. We ask which part of a modern LLM should absorb continual u

Scalable Weak-to-Strong Verifier Hardening for Agent Environments

arXiv:2610.15924v1 Announce Type: cross Abstract: Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn complex outcomes into rewards. Ve

agentsarxiv-cs-lg

Separating Prompt Harmfulness from Interaction Safety via Partial Generation

arXiv:2610.16761v1 Announce Type: cross Abstract: LLM-based information systems need safety guardrails. Most guardrails classify the prompt alone. If the prompt looks harmful, the system rejects it be

Towards Cost-Efficient Video Dataset Construction with Large Language Models

arXiv:2610.16857v1 Announce Type: new Abstract: Research on video classification models has witnessed significant growth recently, accompanied by a significant increase in the demand for datasets to t

researcharxiv-cs-cv

Safety

Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents

arXiv:2610.14683v1 Announce Type: new Abstract: Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing pri

safetyarxiv-cs-lg

TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training

arXiv:2610.17726v1 Announce Type: new Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new sk

Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors

arXiv:2610.15556v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models commonly generate robot actions token by token through Large Language Model (LLM) decoders, causing inference late

VitaBench: Memory Lifecycles Beyond Retrieval

arXiv:2610.14731v1 Announce Type: new Abstract: Long-term memory benchmarks for language agents primarily evaluate whether information from earlier interactions can be retrieved and used when relevant

Cancerous Backdoor Attack: Gradual Activation via Invisible Tokens and A Stage Controller

arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single

researcharxiv-cs-ai

Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits

arXiv:2610.13494v1 Announce Type: new Abstract: Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnes

Applications

RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning

arXiv:2610.13399v1 Announce Type: new Abstract: Long-term personalized tutoring requires language models to translate a learner's evolving history into appropriate instructional support. Existing LLM-

applicationsarxiv-cs-ai

Scalable Circuit Learning for Interpreting Large Language Models

arXiv:2606.16939v2 Announce Type: replace-cross Abstract: A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly prod

Local Ai

Scaling Agentic Data for Long-Horizon Terminal Intelligence

arXiv:2610.13166v1 Announce Type: new Abstract: Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool

local-aiarxiv-cs-cl

SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire

arXiv:2610.12596v1 Announce Type: new Abstract: Agentic large language models now tackle complex tasks, yet strategic reasoning over long horizons is under-studied: most agentic benchmarks are short a

Time Delay System for Sequence Modeling

arXiv:2610.12864v1 Announce Type: new Abstract: Existing sequence models share a critical limitation: they lack an explicit, interpretable mechanism for content-aware temporal recall. LSTMs compress e

researcharxiv-cs-cl

Applications

Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph

arXiv:2610.13695v1 Announce Type: new Abstract: Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable. Despite their advantages, current LLM-

applicationsarxiv-cs-cl

What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents

model-releasesarxiv-cs-ro

arXiv:2610.12561v1 Announce Type: new Abstract: Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests. For embodied agent

Tutorials

When Generative Models Act as Simulators

arXiv:2610.12847v1 Announce Type: cross Abstract: A simulator summarizes an environment in a state that users can read and change. Generative models learn representations of the world without explicit

tutorialsarxiv-cs-ai

X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems

arXiv:2610.13886v1 Announce Type: new Abstract: Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact

AgentFly: Scaling Agentic Reinforcement Learning with Unified Resource System

arXiv:2507.14897v2 Announce Type: replace Abstract: Methods to build LLM agents have evolved from prompt engineering and supervised finetuning to agentic reinforcement learning (agentic RL). However,

9 Oct 2026

Agentic-TTT: Training test-time policy for test-time training

arXiv:2610.12002v1 Announce Type: cross Abstract: Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settin