Search: “llm”

Language Carries the Expert's Impression: Instrument-Anchored LLM Judges Transfer Counseling-Quality Assessment and Beat In-Domain Training

arXiv:2610.08055v1 Announce Type: new Abstract: Automatic assessment of communication quality in dyadic counseling conversations is bottlenecked by data: expert-rated corpora are small and expensive t

researcharxiv-cs-cl

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Agents

One Step at a Time: Trading LLM Autonomy for Process Predictability

DGX agent

arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually

agentsarxiv-cs-ai

7 Oct 2026

Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering

model-releasesarxiv-cs-cv

arXiv:2610.08137v1 Announce Type: cross Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These rout

Tree Navigation Without LLM Summaries: A Matched-Cost Study of Hierarchical Retrieval for Long-Document QA

arXiv:2610.06902v1 Announce Type: new Abstract: Retrieval-augmented generation grounds language models in external context, but for long documents flat top-k retrieval can cluster on a single region a

researcharxiv-cs-cl

Understanding Errors in LLM-Based Question Answering over Imperfect Tables

arXiv:2610.04687v2 Announce Type: replace Abstract: Answering questions over imperfect tables requires handling errors that can affect the answer. We investigate two challenges for large language mode

agentsarxiv-cs-cl

When Does External Guidance Help LLM Reasoning? A Bias-Variance Theory of Guidance-Augmented GRPO

arXiv:2610.06861v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become the dominant paradigm for eliciting multi-step reasoning in large language models, an

Anatomy of LLM Sycophancy: What a Flip Rate Hides

arXiv:2610.06522v1 Announce Type: cross Abstract: A model under pushback can correct itself, capitulate, or hold, and one flip rate counts a correction and a capitulation alike. Using SycoLens, a modu

BARQ: Balanced Codebook Refinement for Low-Bit LLM Quantization

arXiv:2610.04490v1 Announce Type: new Abstract: As large language models (LLMs) grow in parameter count, model storage and parameter memory traffic have become major bottlenecks to efficient deploymen

Don't Judge an LLM Only by Its Activations: Discovering Suppressed Safety Features via Counterfactual Activation Potential

arXiv:2610.05541v1 Announce Type: new Abstract: Mechanistic interpretability has emerged as the primary means to understand safety behavior of LLMs. However, existing tools primarily focus on the acti

Robust Parameter-Efficient LLM Adaptation on Analog Hardware

arXiv:2610.05318v1 Announce Type: new Abstract: Analog in-memory computing is a promising platform for on-device execution of large language models because it performs matrix--vector multiplications (

StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search

arXiv:2610.03799v1 Announce Type: new Abstract: Recovering executable CAD programs from 3D meshes is challenging due to the compositional nature of CAD construction and the interaction between discret

safetyarxiv-cs-cv

BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration

arXiv:2610.02800v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, e

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

model-releasesarxiv-cs-cl

arXiv:2509.20810v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual er

Frequency Is Not Sensitivity Identifying Safety-Sensitive Experts in Sparse MoE LLM

arXiv:2610.02910v1 Announce Type: new Abstract: Suppressing a small set of routed experts can weaken the safety behavior of a sparse Mixture-of-Experts (MoE) language model without retraining. Which e

How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment

arXiv:2607.14197v2 Announce Type: replace Abstract: Artificial Intelligence (AI) answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of p

local-aiarxiv-cs-ai

Mental-R1: Aligning LLM Reasoning for Mental Health Assessment

arXiv:2606.13176v2 Announce Type: replace Abstract: Mental health problems such as anxiety, depression, and suicide remain urgent global challenges, where timely and accurate assessment is critical fo

Power-SMC: Low-Latency Sequence-Level Power Sampling for Training-Free LLM Reasoning

arXiv:2602.10273v3 Announce Type: replace-cross Abstract: Reasoning ability in large language models is often attributed to distribution sharpening: concentrating output probability on high-likelihood

researcharxiv-cs-lg

Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting

model-releasesarxiv-cs-cl

arXiv:2610.03567v1 Announce Type: new Abstract: We describe the Writerslogic team's participation in the CLEF 2026 SimpleText shared task, addressing Task 1 (text simplification) and Task 2 (complexit

False Floors: LLM Safety Routing Evaluations Break Under Distribution Shift

arXiv:2610.01535v1 Announce Type: cross Abstract: Safety routers send each request to one of several models and are judged against the best single model. A major routing benchmark picks that comparato

HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval off

Hardware

MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration

arXiv:2606.22826v2 Announce Type: replace Abstract: Evaluating LLMs across many model variants---quantized, fine-tuned, or deployment-specific---requires running large benchmarks repeatedly, a process

hardwarearxiv-cs-ai

Paying for Too Many Tokens? Valid and Cost-Efficient Multimodal LLM Annotation with Simple Heuristics

arXiv:2610.00809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) enable video annotation at scale, but costs accumulate quickly: processing a typical 60-second short-form video at one fra

researcharxiv-cs-cv

Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

arXiv:2606.13607v3 Announce Type: replace Abstract: When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not

researcharxiv-cs-ai

Rules to Tools: Executable Checks for LLM Agents in Scientific Computing

arXiv:2610.00313v1 Announce Type: new Abstract: Scientific coding agents receive equations, boundary conditions, and output requirements in writing, then must assess the programs they revise. Rules to

agentsarxiv-cs-ai

SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RL

arXiv:2610.02023v1 Announce Type: new Abstract: While Large Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making

local-aiarxiv-cs-ai

System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer

arXiv:2610.00253v1 Announce Type: cross Abstract: Audits of AI visibility summarise the brand recommendations of deployed language models into per-system profiles. We test whether such a profile descr

The Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning

arXiv:2610.00332v1 Announce Type: cross Abstract: Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current a

Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning

arXiv:2610.02190v1 Announce Type: new Abstract: Step-size selection remains a central challenge in large-scale neural network optimization; conservative steps slow convergence, while aggressive steps

local-aiarxiv-cs-lg

When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

arXiv:2610.00372v1 Announce Type: new Abstract: Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Y

Faithful Dual-constrained Erasure for Robust LLM Safety Alignment

arXiv:2609.39279v1 Announce Type: cross Abstract: Machine unlearning has emerged as a crucial mechanism for removing hazardous knowledge and enforcing safety alignment in Large Language Models (LLMs).

Forging LLM Authorship Fingerprints with Targeted Rewriting

arXiv:2609.38831v1 Announce Type: new Abstract: Model-attribution classifiers can often identify which language model produced a text, making model-specific writing patterns a signal of provenance. Ac

researcharxiv-cs-cl

Applications

From Spectra to Joint Schedules in LLM Pre-training: 3+3(+2) Scaling-Law Regimes

arXiv:2609.40148v1 Announce Type: new Abstract: Power-law learning curves are often treated as fixed properties of a model and its data, although learning-rate and batch-size schedules can change the

applicationsarxiv-cs-lg

Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning

arXiv:2609.40286v1 Announce Type: cross Abstract: Unlearning a fact in one language does not guarantee its removal in others as changing the query or even the requested answer language can reopen seem

MoEless: Efficient MoE LLM Serving with Serverless Experts

arXiv:2603.06350v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale efficiently under stringent resource constrain

local-aiarxiv-cs-ai

TomasuLLM: Out-of-Order Speculative Execution for LLM Agents

model-releasesarxiv-cs-cl

arXiv:2609.38201v1 Announce Type: new Abstract: Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. Thi

A Spectral Theory of Distortion in LLM Graph Reconstruction: Sharp Bounds and Empirical Characterization

arXiv:2609.38161v1 Announce Type: new Abstract: Evaluations of graph reconstruction by language models typically report a single aggregate distance between the original and the reconstructed graph. We

safetyarxiv-cs-lg

AnthroDial: Benchmarking LLM Anthropomorphism in Autonomous Social Interaction

arXiv:2609.37853v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or pers

agentsarxiv-cs-cl

Authority Before Utility: Non-Compensatory Control for Persistent LLM Memory

arXiv:2609.37474v1 Announce Type: new Abstract: Persistent memory creates a control problem that retrieval relevance alone does not solve: a memory can remain highly useful after an update, deletion,

researcharxiv-cs-ai

Causal and Interpretable Structures in LLM Compositional Tasks

arXiv:2609.35970v1 Announce Type: cross Abstract: Large language models are able to solve tasks whose answers depend on not only individual input tokens, but also on relations among them. How is such

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

arXiv:2609.36689v1 Announce Type: cross Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that

Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?

arXiv:2609.35868v1 Announce Type: new Abstract: Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations can

LazySloth: Bounded LLM-based Lazy Tree Search for Fast Long Video Comprehension

arXiv:2609.37426v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture. H

Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents

arXiv:2609.35911v1 Announce Type: cross Abstract: Adapting large language model agents during deployment requires not only retaining past experience, but also turning new observations into timely guid

Local Predictability and Collective Fidelity in LLM-Agent Societies

arXiv:2609.35813v1 Announce Type: cross Abstract: Compact surrogates could reduce the cost of simulating large language model societies, but must reproduce collective behavior. We compare individual p

Normalize-Then-Precondition: A Hierarchical Approach to Marginal Scale and Interaction Geometry for LLM Training

arXiv:2609.36692v1 Announce Type: cross Abstract: Matrix optimizers have emerged as a promising direction, with Muon standing out as a prominent design. Revisiting Muon through its full-Gram represent

Overcoming Scaling Limits in On-Policy Self-Distillation for LLM Reasoning

arXiv:2609.37915v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) trains a student to match a privileged teacher distribution along its own sampled trajectory. Standard OPSD applies

Pair Difficulty Matters: Rethinking Pairwise LLM-as-a-Judge Evaluation and Consistency

arXiv:2609.37577v1 Announce Type: new Abstract: Large Language Model judges are widely used to rank texts and text-generating systems through pairwise comparison, and their reliability is typically as

Principled Thoughts for Latent Recursive LLM Systems

arXiv:2609.36159v1 Announce Type: new Abstract: Large language models can reason in continuous space instead of decoded text, by recurring on their own hidden states or by passing those states between

agentsarxiv-cs-ai