Search: “llm”

Evaluating Criterion-Conditioned Behaviour of Large Language Models in Content Moderation

model-releasesarxiv-cs-cl

arXiv:2609.03814v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance on standard content moderation benchmarks. However, these benchmarks often aggregate multipl

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HalluPeer: A Taxonomy-driven Benchmark for Detecting Hallucinations in Scientific Peer Reviews

DGX agent

arXiv:2609.03580v1 Announce Type: new Abstract: The growing scale of academic peer review has motivated the use of Large Language Models (LLMs) as review assistants, yet LLMs can generate fluent but u

model-releasesarxiv-cs-ai

4 Sept 2026

Interface-Induced Trajectory Censoring

arXiv:2609.03966v1 Announce Type: new Abstract: Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model is emitting well-formed calls: the interfa

PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

arXiv:2609.02896v1 Announce Type: new Abstract: Medical relation extraction (MRE) is commonly known for extracting entities and their relations jointly from a medical text, which has attracted conside

researcharxiv-cs-cl

Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL

arXiv:2609.02944v1 Announce Type: cross Abstract: Democratizing data access through natural language is a crucial goal for modern enterprises, but the practical adoption of Text-to-SQL is critically h

The Natural Language Interaction Protocol and Standard for AI Agents

arXiv:2609.04135v1 Announce Type: new Abstract: AI agents are increasingly being developed and deployed across organizations using heterogeneous agent-development frameworks, AI models, tool interface

Toward Collective-Centric Evaluation of Preference Inference for Participatory Democracy

arXiv:2609.02990v1 Announce Type: cross Abstract: To scale up collective decision-making, participatory democracy platforms such as Polis and Remesh enable online deliberation among thousands of parti

When Optimization Becomes Manipulation: Defending Generative Search against Malicious Generative Engine Optimization

arXiv:2609.02964v1 Announce Type: cross Abstract: This paper focuses on defending generative search engines against malicious Generative Engine Optimization (GEO), which rewrites web documents to matc

Automated Vulnerability Injection in Smart Contracts Using Large Language Models

arXiv:2609.02624v1 Announce Type: cross Abstract: Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to b

Beyond Context Windows: Persistent Discovery Context for Data-Centric Agents

arXiv:2609.02129v1 Announce Type: new Abstract: Data-centric agents repeatedly perform a discovery step before planning or execution: identifying the data objects relevant to a task. Yet successful di

Cliff: Learning Process Rewards from the First Mistake

arXiv:2609.02817v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance

safetyarxiv-cs-lg

CodePoisonRAG: Knowledge Poisoning Attacks on Retrieval-Augmented Code Generation

arXiv:2609.02774v1 Announce Type: cross Abstract: Retrieval-Augmented Code Generation (RACG) improves LLM-based software development by retrieving external code artifacts, documentation, and patches,

safetyarxiv-cs-lg

Dictionary-Guided Mutation Operators for Automated HDL Repair

arXiv:2609.01775v1 Announce Type: cross Abstract: Automated repair of Hardware Description Language (HDL) designs remains challenging due to the large search space of candidate repairs and the strict

HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models

arXiv:2609.02029v1 Announce Type: new Abstract: Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughp

Tutorials

Induction and Inquiry via Probabilistic Reasoning over Language and Code

arXiv:2609.01815v1 Announce Type: new Abstract: How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. An

tutorialsarxiv-cs-ai

Interpretable Symptom Vectors for Depression in a Large Language Model

arXiv:2609.01832v1 Announce Type: cross Abstract: Patients with depression present with diverse symptom profiles, yet clinical practice routinely reduces this variation to a single severity score. Lar

Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting

arXiv:2609.02649v1 Announce Type: new Abstract: Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundamental challenge when deploying NLP systems in real-world industri

OmegaUse-SOP: SOP Engineering for Professional Computer Use from Human Demonstrations

arXiv:2609.02149v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evolving from conversational assistants into agents capable of operating external digital environments.

PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation

arXiv:2609.02272v1 Announce Type: cross Abstract: Faithfully translating research papers into repository-level implementations remains challenging because papers often describe methods at a high level

Applications

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: A Comparative Study

arXiv:2507.08468v2 Announce Type: replace Abstract: This paper provides an experimental evaluation of the capability of large language models (LLMs) to assist in legal decision-making within the frame

applicationsarxiv-cs-cl

When Agents Implement Systems: A Case Study in Defects, Detection, and Evaluation Rigor

arXiv:2609.01985v1 Announce Type: new Abstract: As LLM coding agents increasingly perform end-to-end engineering work, we lack empirical characterization of how they behave on systems-level requiremen

When Persona Attributes Improve Population Alignment in Large Language Models

arXiv:2609.02526v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting

safetyarxiv-cs-cl

Agentic Empirical Asset Pricing: Methodological Foundations

arXiv:2609.00731v1 Announce Type: new Abstract: Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously c

Agentic programs: an emerging form of scientific software in computational materials science

arXiv:2609.00795v1 Announce Type: cross Abstract: Computational materials science has traditionally delegated algorithmic tasks to computers while leaving scientific judgments to humans. We argue that

Asymmetries in Spontaneous and Instructed Deception

arXiv:2609.00180v1 Announce Type: new Abstract: Large language models sometimes deceive users without being instructed to. However, much of the study on deception in models involves instructed decepti

BiasGym: A Simple and Generalizable Framework for Analyzing and Removing Biases through Injection

arXiv:2508.08855v5 Announce Type: replace-cross Abstract: Understanding biases and stereotypes encoded in the weights of Large Language Models (LLMs) is crucial for developing effective mitigation str

Hardware

CacheBridge: Efficient Cross-Model KV Cache Transfer

arXiv:2609.00891v1 Announce Type: new Abstract: Sharing context between LLMs in a multi-model system requires the receiving model to prefill the shared prefix because KV caches are model-specific. Rec

hardwarearxiv-cs-ai

Cheap Verifiers, Large Blind Spots: Measuring the Reliability Cost of Cost-Saving Cascades

arXiv:2609.01345v1 Announce Type: new Abstract: Inference cascades cut cost by answering most queries with a cheap model and escalating a hard tail to a frontier model that acts as verifier. A natural

Detoxifying Toxic Communication: A Design Science Approach to Responsible AI

arXiv:2609.00361v1 Announce Type: cross Abstract: Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Ex

safetyarxiv-cs-cl

DiagEvo: Diagnosis-Guided Self-Evolution via Hierarchical Error Memory

arXiv:2609.00768v1 Announce Type: new Abstract: Self-play is an effective paradigm for language-model self-evolution, but without guidance, solver performance can plateau or decline across rounds. Ung

Does task decomposition improve automatic NLG evaluation?

arXiv:2609.01139v1 Announce Type: new Abstract: The LLM-as-a-judge (LLMaJ) framework has emerged as a promising solution for cheap, reproducible, reference-free Natural Language Generation (NLG) evalu

researcharxiv-cs-cl

Dual Process Motion Planning

arXiv:2609.01260v1 Announce Type: new Abstract: Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with speed, precision, and reliability. Classical

Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches

arXiv:2609.00946v1 Announce Type: cross Abstract: Conditional independence tests (CITs) test for conditional dependence between two random objects X and Y given a third random object Z. Existing CITs

From Legible to Inscrutable Trajectories: (Il)legible Motion Planning Accounting for Multiple Observers

arXiv:2602.09227v2 Announce Type: replace Abstract: In cooperative environments, such as in factories or assistive scenarios, it is important for a robot to communicate its intentions to observers, wh

agentsarxiv-cs-ro

GRRM: Group Relative Reward Modeling for Machine Translation

arXiv:2602.14028v2 Announce Type: replace Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Mach

safetyarxiv-cs-cl

HiveTraceGuard-Pro: A Compact Generative Guardrail for Prompt Injection, Jailbreaks, and Adversarial Obfuscation

arXiv:2609.01046v1 Announce Type: cross Abstract: Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigati

Invalidation Contracts for Cross-Episode Agent Memory

arXiv:2609.00243v1 Announce Type: new Abstract: LLM agents that cache recovery suggestions from API errors can skip re-derivation in later episodes, spending fewer tokens and fewer model calls on cons

Making Prospective Memory SLM-Shaped: Typed Intention Stores for Small-Model Agents

arXiv:2609.01272v1 Announce Type: new Abstract: Prospective memory means carrying out a deferred intention at the right future cue while other work continues. Benchmarks now isolate it as an agent ski

MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems

arXiv:2602.03053v2 Announce Type: replace Abstract: Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, w

Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations

arXiv:2609.01257v1 Announce Type: new Abstract: As LLM-based human simulators are increasingly used for policy, evaluation, and training, they must faithfully reproduce real behavioral patterns. While

safetyarxiv-cs-ai

Neurosymbolics for Data Engineering: Achieving Long Context Token Reduction Without Finetuning

arXiv:2609.00367v1 Announce Type: cross Abstract: Large Language Models are increasingly deployed for sophisticated data engineering tasks such as generating structured queries from natural language,

Patterning in Practice: Debiasing Reward Models with Susceptibilities

model-releasesarxiv-cs-lg

arXiv:2609.00699v1 Announce Type: new Abstract: Reward models trained on human preferences are known to suffer from length, formatting, and other stylistic biases. In this paper we use patterning, whi

Polished but Unresolved: Identifying Late-Stage Pressure States in Long-Horizon Tool-Use Agents

arXiv:2609.00823v1 Announce Type: new Abstract: Long-horizon tool-use agents need not only to search and plan, but also to decide when to finalize. We study late-stage pressure states, in which an age

Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reports

arXiv:2607.17122v2 Announce Type: replace Abstract: Scope 3 greenhouse gas (GHG) emissions account for the majority of corporate carbon footprints, yet remain difficult to analyze at scale due to spar

local-aiarxiv-cs-cl

Solaris: Towards Interfaces That Are Generated, Not Coded

arXiv:2609.00776v1 Announce Type: cross Abstract: Digital interfaces are traditionally implemented through intermediate representations such as code, requiring their appearance and behavior to be spec

Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

arXiv:2609.00319v1 Announce Type: cross Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose

researcharxiv-cs-cl

The Answer Is Not the Argument

arXiv:2609.00264v1 Announce Type: new Abstract: Chain-of-thought monitoring is proposed for AI oversight, yet evaluations often provide monitors with a trusted reference answer. We ask whether answer

Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search

arXiv:2609.00618v1 Announce Type: cross Abstract: We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regar