Search: “llm”

Beyond Unsafe Detection: Counterfactually Anchored Evidence Attribution for Multi-Turn LLM Safety Failures

arXiv:2609.27773v1 Announce Type: cross Abstract: As Large Language Models (LLMs) move from conversational assistants to advanced agentic systems, guardrail failures can convert adversarial intents in

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Count Evidence, Not Sentences: Tempered Evidence Fusion of LLM Judgments for Long-Text Value Measurement

DGX agent

arXiv:2609.27165v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix backgro

model-releasesarxiv-cs-ai

24 Sept 2026

Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents

arXiv:2609.27334v1 Announce Type: new Abstract: Agentic memory systems reuse past experience to improve future performance, yet most existing designs curate memory at write time: once a task is comple

agentsarxiv-cs-ai

KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling

arXiv:2609.27294v1 Announce Type: cross Abstract: Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, p

agentsarxiv-cs-ai

Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors

arXiv:2609.27051v1 Announce Type: new Abstract: Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire

ARAFA: An LLM-Generated Arabic Fact-Checking Dataset

arXiv:2609.25833v1 Announce Type: new Abstract: Automatic fact-checking poses a significant challenge in Arabic natural language processing due to the scarcity of datasets and resources. In this manus

'As a Language Model...': Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It

arXiv:2609.25021v1 Announce Type: cross Abstract: Large Language Models (LLMs) tend to add disclaimers like 'I'm just an AI' when asked about something related to themselves. The self-reports from suc

safetyarxiv-cs-cl

Local Ai

Beyond Repeated Sampling: Learning Search Policies for LLM Reasoning

arXiv:2609.26704v1 Announce Type: new Abstract: Large language models increasingly tackle hard reasoning problems by spending more test-time compute, yet the dominant strategy remains naive repeated s

local-aiarxiv-cs-cl

Block-Level Weight-Space Structure Persists Under Post-Training: An Empirical Study Across LLM Families

arXiv:2609.26147v1 Announce Type: new Abstract: Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present

ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning

arXiv:2609.25058v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) adapts large language models (LLMs) to downstream tasks while updating only a small fraction of their pretrained

Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code

arXiv:2608.28021v2 Announce Type: replace-cross Abstract: Large language models increasingly author Infrastructure-as-Code (IaC), where one insecure default is provisioned straight into production. Pr

Disaggregated Quantization: Specializing LLM Prefill and Decode

arXiv:2609.26333v1 Announce Type: new Abstract: Prefill and decode reward different approaches to quantization: low-precision arithmetic accelerates prompt processing, while compact weights reduce mem

Graph Memory for LLM Agents: At What Cost? A Comparative Evaluation of Query, Ingest, and Update Performance Across Graph Database Engines

arXiv:2609.23315v1 Announce Type: cross Abstract: Graph databases are frequently positioned as categorically necessary for connected-data workloads, yet the systems dimension along which they actually

Greedy Decoding Is Not Precision-Invariant: Cross-Precision Output Divergence in LLM Inference

arXiv:2609.26621v1 Announce Type: new Abstract: Greedy decoding from large language models is commonly treated as deterministic. We show it is not precision-invariant: the same model, prompt, and deco

hardwarearxiv-cs-lg

Local Ai

Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibratione

arXiv:2609.25049v1 Announce Type: new Abstract: Large language models (LLMs) aligned for safety often suffer from over-refusal, incorrectly rejecting benign yet safety-related instructions. Prior stud

local-aiarxiv-cs-cl

Passes Alone, Fails Together: Benchmarking Semantic Coordination in Parallel LLM-Agent Development

arXiv:2609.25396v1 Announce Type: new Abstract: Parallel coding agents can produce patches that work alone but fail when merged. This happens when one agent changes an interface or rule that another a

PhysAI-Bench: A Benchmark for LLM-Based Agentic Decision-Making in Autonomous UAV-Centric Physical AI

arXiv:2609.23695v1 Announce Type: new Abstract: Recent advances in Physical AI have accelerated the use of foundation models in autonomous systems such as unmanned aerial vehicles (UAVs), which must p

Splitting Documents at Lower Cost: Multi-Split Boundary Decisions for LLM-Based Page Stream Segmentation

arXiv:2609.22620v1 Announce Type: new Abstract: Scanned mail, uploaded PDFs, and consolidated attachments often arrive as page streams that must be split into individual documents before downstream cl

researcharxiv-cs-ai

Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management

arXiv:2509.26200v2 Announce Type: replace-cross Abstract: Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a shar

Tutorials

Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint

arXiv:2508.20443v3 Announce Type: replace Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership c

tutorialsarxiv-cs-lg

Trustworthy Agentic AI: Failure Modes, Mitigation Strategies, and a Lifecycle Framework for Autonomous LLM Systems

arXiv:2609.22712v1 Announce Type: new Abstract: Agentic AI systems built on large language models can plan over multiple steps, use external tools, retain information in memory, and coordinate with ot

Apollo Restore: A Foundation LLM for Historical Greek Optimized for Fill-in-the-Middle Restoration of Ancient Greek Texts

arXiv:2609.22455v1 Announce Type: new Abstract: We present Apollo Restore, a 24-billion-parameter large language model for restoring lacunae---physical gaps---in fragmentary Ancient Greek texts. Fine-

Applications

Do Chess Explanations Reflect Model Decisions? Behavioral and Token-Level Tests of LLM Reasoning Faithfulness

arXiv:2609.22245v1 Announce Type: new Abstract: Large language models can produce fluent explanations for chess moves, but plausible language does not necessarily reflect the reasoning behind a decisi

applicationsarxiv-cs-cl

Efficient LLM Distillation for Bangladesh Legal Context: A Smartphone-Compatible Retrieval-Augmented Generation Model

arXiv:2609.24177v1 Announce Type: new Abstract: Legal information in Bangladesh is inaccessible to most citizens. Statutory text is English-only, trained lawyers are concentrated in urban centres, and

GRRR: The Geometry of Reshaping, Rotation, and Routing in Decoder LLM post-training

arXiv:2609.22146v1 Announce Type: cross Abstract: We study how post-training changes the weights of Large Language Models (LLMs) relative to their pretrained weights. Across 12 post-training chains wi

Tutorials

LLM-Based FORM Code Generation with Verification-Driven Fine-Tuning

arXiv:2609.23367v1 Announce Type: cross Abstract: FORM is a domain-specific symbolic manipulation language widely used in particle physics for processing the very large algebraic expressions arising f

tutorialsarxiv-cs-cl

Luck Is Not Skill: When Do Paired Rollouts Help Group-Relative RL of LLM Agents?

arXiv:2609.24144v1 Announce Type: new Abstract: Group-relative reinforcement learning compares rollouts of the same prompt, but independent environment noise can obscure these comparisons. We study pa

agentsarxiv-cs-lg

Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents

arXiv:2601.15322v3 Announce Type: replace-cross Abstract: Tool-using agents can repeat a final decision while changing their recorded execution. We introduce the Determinism-Faithfulness Assurance Har

Semantics Delivery Network: Rethinking Web Retrieval Infrastructure for LLM Agents

arXiv:2609.22486v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external sources when answering questions that require proprietary information or up-to-date live we

agentsarxiv-cs-lg

Strategy Accumulation and Guided Execution for Automated LLM Fine-Tuning

arXiv:2609.22257v1 Announce Type: cross Abstract: Producing task-specific large language models requires discovering effective training strategies through experimentation. Automated fine-tuning system

agentsarxiv-cs-cl

Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks

arXiv:2609.23435v1 Announce Type: cross Abstract: Multi-omics sequences contain complex biological patterns, yet deciphering their mechanisms for automated scientific discovery remains challenging. As

TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes

arXiv:2609.23766v1 Announce Type: cross Abstract: Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators c

hardwarearxiv-cs-lg

What are Key Factors for Updates in RL for LLM Reasoning?

arXiv:2606.22570v2 Announce Type: replace Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning ability of large language mod

safetyarxiv-cs-cl

From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News

arXiv:2609.20838v1 Announce Type: new Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-end

How Much of a Real Workload Can LLM-Generated GPU Kernels Actually Reach?

arXiv:2609.21058v1 Announce Type: cross Abstract: Language models can now write GPU kernels that outperform PyTorch. We evaluate five model configurations on KernelBench level 1 and find that a fronti

hardwarearxiv-cs-ai

SynthDemo-RL: Breaking the Zero-Reward Barrier in VLA Adaptation with LLM-Guided Synthetic Demonstrations

arXiv:2609.21650v1 Announce Type: cross Abstract: Fine-tuning Vision-Language-Action (VLA) models commonly relies on human teleoperation demonstrations, while reinforcement learning (RL) with sparse b

Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals

arXiv:2609.21450v1 Announce Type: new Abstract: Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains d

A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities

arXiv:2603.02540v2 Announce Type: replace Abstract: Large language models (LLMs) display a unified 'general factor' of capability across 10 benchmarks (a finding confirmed by our factor analysis of 15

Behavioral Coherence: A Method for Sensitive-Domain LLM Evaluation

arXiv:2512.13142v5 Announce Type: replace Abstract: People use LLMs for reproductive-health questions, including abortion-related support. A response can sound supportive while answers reinforce harmf

Benchmarking LLM Compliance with China AI Generated Content Regulations

arXiv:2609.19989v1 Announce Type: new Abstract: The widespread adoption of LLMs has led to escalating content compliance risks. Prior works have contributed to addressing these risks in the English co

Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data

arXiv:2609.19805v1 Announce Type: new Abstract: Grapheme-to-phoneme (G2P) conversion turns raw text into its phonemic form and is an essential part of both text-to-speech (TTS) and automatic speech re

How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents

arXiv:2609.20474v1 Announce Type: new Abstract: Agent harnesses supply planning guidance, organize execution, and check completion. We study how these components affect success, erroneous acceptance,

KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms

arXiv:2609.19916v1 Announce Type: cross Abstract: Large language models (LLMs) are typically evaluated on static benchmarks, even though natural language constantly evolves through newly emerging word

Quantifying Overclaiming Propensity in Frontier LLM Agents

arXiv:2609.20812v1 Announce Type: cross Abstract: Frontier coding agents are increasingly trusted to work autonomously for long periods, yet an agent's final response is often the only account of that

agentsarxiv-cs-ai

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

arXiv:2609.19499v1 Announce Type: new Abstract: Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses. In sampling-based methods, the in

CompileRover: Revolutionizing Virtual Machine Compiler Optimization with a Tri-Role LLM-Driven Framework

arXiv:2609.19004v1 Announce Type: cross Abstract: Code optimization plays a crucial role in the development of virtual machine compilers, with optimization frameworks significantly enhancing the perfo

17 Sept 2026

GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference

arXiv:2609.17573v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are emerging as a promising generative paradigm that complements autoregressive decoding. In long-context sett

hardwarearxiv-cs-lg

17 Sept 2026

Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant

arXiv:2609.17546v1 Announce Type: new Abstract: In this position paper, we argue that legal LLMs' hallucinations should be evaluated as a failure of legal warrant rather than as factual inaccuracy or