Search: “llm”

LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents

arXiv:2606.18388v2 Announce Type: replace-cross Abstract: RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically acros

4 Sept 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Applications

The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis

DGX agent

arXiv:2609.03218v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalizati

applicationsarxiv-cs-cl

4 Sept 2026

Local Ai

Unfolding the Leech Lattice: Fused Multi-Shell Decoding and VRAM Layouts for 2-Bit LLM Weights

arXiv:2609.02652v1 Announce Type: new Abstract: Leech-lattice vector quantization holds the strongest reported 2-bit quality under its own evaluation protocol. Its kernel decodes one shell; we found n

local-aiarxiv-cs-lg

3 Sept 2026

XMerge: Cross-Axis Selection and Reconstructive Layer Merging for LLM Depth Compression

arXiv:2609.02083v1 Announce Type: cross Abstract: Removing complete transformer layers preserves a standard serving architecture, but existing depth-compression methods can lose substantial quality, a

3 Sept 2026

Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM

arXiv:2609.00231v1 Announce Type: new Abstract: Existing research on object hallucination in multimodal large language models (MLLMs) predominantly attributes the problem to language priors such as ov

researcharxiv-cs-cv

Applications

Confess What You Know: Forget-Set Misalignment with Model Knowledge in LLM Unlearning

arXiv:2609.00605v1 Announce Type: cross Abstract: Machine unlearning for large language models (LLMs) often assumes that a pre-defined forget set matches what the model has memorized, but this frequen

applicationsarxiv-cs-ai

Emotional Labor Strategy Preferences in LLM Personas

arXiv:2609.00310v1 Announce Type: new Abstract: Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated w

Frozen Cores Need Task Signal: Fisher-Whitened Cross-Covariance for Low-Resource LLM Adaptation

model-releasesarxiv-cs-lg

arXiv:2609.00762v1 Announce Type: new Abstract: Parameter-efficient fine-tuning is usually framed as a question of how many parameters to update. Under a severe trainable-state budget, however, where

HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference

arXiv:2609.00450v1 Announce Type: cross Abstract: Block Quantization (BQ) is a promising approach for efficient deployment of large language models (LLMs), enabling low-precision computation with cont

LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts

arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how a

LLM-Driven Autonomous Vehicles Inherit Human Driver Biases in Pedestrian Yielding: Results and Implications From A New Benchmark

arXiv:2609.00192v1 Announce Type: new Abstract: Public trust in Autonomous Vehicles (AVs) may depend not only on technical success but also on the fairness of their decision making. While a recent tre

Tutorials

Pre-carved Niches: The Formation Dynamics of Modular Task Partitions in Early LLM Training

arXiv:2609.01170v1 Announce Type: new Abstract: Large language models exhibit a modular internal organization that mirrors well-studied functional networks of the human brain, but how this organizatio

tutorialsarxiv-cs-lg

RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving

arXiv:2609.00062v1 Announce Type: cross Abstract: Data contamination undermines the reliable evaluation of large language models (LLMs) on mathematical problem solving. While rewriting-based evaluatio

A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization

arXiv:2604.01452v2 Announce Type: replace Abstract: Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, including

agentsarxiv-cs-ai

AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning

arXiv:2608.29988v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are commonl

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

model-releasesarxiv-cs-lg

arXiv:2608.30141v1 Announce Type: cross Abstract: Preference optimization is widely used to align large language models with human preferences, but preference-data composition may also influence priva

Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning

arXiv:2602.01695v2 Announce Type: replace Abstract: Latent reasoning reduces the token-generation cost of chain-of-thought reasoning by replacing explicit intermediate tokens with continuous latent tr

BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing

arXiv:2608.31105v1 Announce Type: new Abstract: Users of a deployed language model routinely encounter behaviours that testing almost never surfaces, since deployment puts the model through orders of

safetyarxiv-cs-ai

Breaking Darknet CAPTCHAs with general purpose LLM

arXiv:2608.28794v1 Announce Type: cross Abstract: Our work evaluates the effectiveness of automated methods for solving CAPTCHA challenges commonly encountered in darknet environments. These CAPTCHAs

safetyarxiv-cs-cv

Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?

arXiv:2608.30948v1 Announce Type: new Abstract: LLMs can imitate how people write, which raises concerns about impersonation, trust, and detection in social settings. These concerns are especially imp

Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment

arXiv:2608.29978v1 Announce Type: new Abstract: Large language models are increasingly required to generate responses that satisfy multiple competing objectives. Since optimal trade-offs depend on bot

GMTS: Gradient Magnitude-based Token Selection Improves RLVR Training for LLM Reasoning

arXiv:2608.30632v1 Announce Type: cross Abstract: Reinforcement learning (RL), particularly RL with Verifiable Rewards (RLVR), has recently emerged as a central paradigm for enhancing large language m

Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents

arXiv:2608.30322v1 Announce Type: new Abstract: Professional agent tasks often depend on conventions that are absent from public corpora, yet benchmarks rarely control whether an agent has access to t

agentsarxiv-cs-ai

LLM Post-Training as Brownfield Maintenance: An Industrial Perspective on Dataware Engineering

arXiv:2608.31102v1 Announce Type: cross Abstract: Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixtu

Personas Differ from Native-Language Generation: Language Pathways Shape LLM Interpersonal Advice

arXiv:2608.30873v1 Announce Type: cross Abstract: LLMs are increasingly used for interpersonal advice and as tools for studying social behavior across languages and cultures. A common shortcut for eli

Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity

arXiv:2505.17537v2 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect answers with high confidence, yet the factors associated with such overconfidence remain insuff

Real-Time Deadlines Reveal Fragile Temporal Adaptation in LLM Strategic Dialogues

arXiv:2601.13206v2 Announce Type: replace Abstract: Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiat

SemTrace: Source-Grounded Semantic Signatures for Tracing LLM Exposure to Protected Documents

arXiv:2608.29575v1 Announce Type: new Abstract: Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot

TACS: Trajectory-Aware Candidate Selection for LLM Jailbreak Suffix Optimization

arXiv:2608.29564v1 Announce Type: new Abstract: Gradient-based jailbreak suffix optimization methods typically update the suffix by retaining the candidate with the lowest current loss. We show that t

Token Counts Are Not Model Lineage: A Frozen-Threshold Holdout Study of Black-Box LLM API Fingerprinting

arXiv:2608.29930v1 Announce Type: new Abstract: Black-box model attribution is increasingly relevant when large language models (LLMs) are served through relay and reseller APIs. A tempting low-cost s

When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models

arXiv:2608.30395v1 Announce Type: new Abstract: As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inf

Accelerating LLM Inference via Vector Index Based Output Embeddings

arXiv:2608.27460v1 Announce Type: new Abstract: Large output embedding matrices create a significant memory bandwidth bottleneck during autoregressive decoding, especially for compact LLMs with large

Auditing Generative Audio Calls for Known-Task Audio-LLM Evaluation

arXiv:2608.27817v1 Announce Type: cross Abstract: Speech and audio LLMs are often evaluated by asking whether a waveform prompt beats an automatic speech recognition (ASR) transcript. For known closed

MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel Operation

arXiv:2608.28300v1 Announce Type: new Abstract: Robotic industrial panel operation requires not only accurate control localization but also compliance with operating procedures, safety rules, and devi

safetyarxiv-cs-ro

NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry

arXiv:2608.28481v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated strong capabilities in natural language understanding and mathematical reasoning. Howe

BekchiAI: Measuring, Observing, and Controlling LLM Agents in One Click

arXiv:2608.26867v1 Announce Type: new Abstract: Large language model agents reason, call tools, and act autonomously over many steps, but their agentic skills-correctly sequencing tools, planning unde

HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement

arXiv:2505.15740v2 Announce Type: replace-cross Abstract: Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their

MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering

arXiv:2601.15236v2 Announce Type: replace Abstract: Large language models can memorize competing locale-specific facts yet fail to select among them when the locale changes, defaulting instead to a si

The Reasoning Tax: Token Economics of LLM Reasoning Across Task Types and Deployment Contexts

arXiv:2608.26235v1 Announce Type: new Abstract: Accuracy-only benchmarking of reasoning-capable large language models misses a central deployment question: when do extended thinking tokens earn their

A Self-Evolving Multi-Agent Framework Defense against LLM Jailbreak Attacks

arXiv:2608.26008v1 Announce Type: cross Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks that exploit techniques such as role-playing, obfuscation, code transformation, an

Can We Read the Mind of an Audio LLM? A Verbalizable, Multilingual Middle-Layer Workspace

arXiv:2608.24958v1 Announce Type: cross Abstract: An audio language model is a black box in a specific way: we see what it says, never what it works out on the way there, and chain-of-thought monitori

MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms

arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impa

agentsarxiv-cs-lg

MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation

arXiv:2608.25085v1 Announce Type: new Abstract: Clinical diagnosis is fundamentally interactive and incremental, yet the dominant paradigm for evaluating Large Language Models (LLMs) in medicine remai

agentsarxiv-cs-cl

Confident at the moment of action: belief miscalibration in LLM play under hidden information

arXiv:2608.24691v1 Announce Type: new Abstract: Agentic systems increasingly gate actions on a model's own stated confidence, which assumes confidence tracks correctness at the moment of acting. We te

agentsarxiv-cs-ai

EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals

arXiv:2608.24086v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as code agents for scientific and engineering analysis, but their ability to analyze raw physical-lay

Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training

arXiv:2604.01597v2 Announce Type: replace Abstract: Traditional RL algorithms like Proximal Policy Optimization (PPO) typically train on the entire rollout buffer, operating under the assumption that

safetyarxiv-cs-lg

NeuronGuard: Robust LLM Safety Alignment via Ablation-Aware Safety Signal Redistribution

arXiv:2608.23959v1 Announce Type: cross Abstract: Safety alignment in large language models (LLMs) remains brittle against a growing spectrum of attacks. Jailbreak attacks bypass safety mechanisms thr

safetyarxiv-cs-ai

Quasar: A Programming Language Specialized for LLM Code Actions

arXiv:2506.12202v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often call external tools to solve tasks. One effective strategy is for LLMs to write code, enabling them to use