Search: “llm”

arXiv:2607.21756v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in database-backed applications to classify tuples, filter records using semantic predicates, extra

applicationsarxiv-cs-lg

27 Jul 2026

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Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions

DGX agent

arXiv:2607.21635v1 Announce Type: new Abstract: Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user. Existing agent benchmarks often eva

model-releasesarxiv-cs-lg

27 Jul 2026

An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models

arXiv:2607.21292v1 Announce Type: new Abstract: We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decom

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles

arXiv:2607.20768v1 Announce Type: cross Abstract: Majority voting over LLMs is widely assumed to benefit from diversity, and diversity measures are used to choose which models to combine. We ask wheth

Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets

arXiv:2607.20441v1 Announce Type: new Abstract: Every information ecosystem produces beliefs that shape strategic decisions. Both human analysts and AI systems inherit the blind spots of their informa

safetyarxiv-cs-cl

Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning

arXiv:2607.21558v1 Announce Type: new Abstract: Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy

Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception

arXiv:2607.20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.

DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions

arXiv:2607.20469v1 Announce Type: new Abstract: Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general structure, if a

Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering

arXiv:2607.20487v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings

GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries

model-releasesarxiv-cs-cl

arXiv:2607.20757v1 Announce Type: cross Abstract: Transformers are known to have internal continuous symmetries that leave outputs invariant, while modifying quantization. GaugeQuant leverages this in

Tutorials

Making Open-Source Text LLM Watermarks Durable Against Merging

arXiv:2607.20435v1 Announce Type: cross Abstract: Open-source LLMs (OSMs)arereaching near state-of-the-art performance, prompting prior works to trace the text they generate by embedding text watermar

tutorialsarxiv-cs-ai

Applications

Safeguards for Speech2Speech LLM-Assistants: A Case Study in Automotive Applications

arXiv:2607.21180v1 Announce Type: new Abstract: Recent advances have introduced speech-to-speech (S2S) conversational assistants capable of producing natural-sounding interactions, including non-verba

applicationsarxiv-cs-ai

Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text

model-releasesarxiv-cs-cv

arXiv:2607.21072v1 Announce Type: new Abstract: Spatial intelligence is essential for agents to move from static semantic understanding toward interacting with the physical world. Many spatial tasks a

Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts

model-releasesarxiv-cs-cl

arXiv:2607.09999v2 Announce Type: replace Abstract: We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Using a six-catego

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

arXiv:2607.21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations. To evaluate unlearning e

Agents

Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts

arXiv:2607.19629v1 Announce Type: cross Abstract: Large language models operating in emotionally sensitive contexts face a structural trilemma: when users in vulnerable states request information that

agentsarxiv-cs-ai

Co-Evolving LLM Evaluators and Policies via DynamicRubric

arXiv:2607.20083v1 Announce Type: cross Abstract: Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models. As policies improve,

Meta-Learning Preferences for Multilingual LLM Alignment

arXiv:2607.13315v2 Announce Type: replace Abstract: Unequal availability of human preference data across languages poses a significant challenge for aligning large language models in multilingual sett

safetyarxiv-cs-cl

Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning

arXiv:2607.19408v1 Announce Type: new Abstract: Using Evolutionary Strategies (ES) for fine-tuning large language models is attractive because it is memory-efficient, parallel, and compatible with bla

researcharxiv-cs-lg

Accuracy Without Grounding: Diagnosing Visual Dependency Dissociation in Video LLM Benchmarks

arXiv:2607.13305v1 Announce Type: cross Abstract: Benchmark accuracy in video large language models (LLMs) is often treated as evidence of visual understanding. We audit this assumption across twenty

Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution

arXiv:2607.13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises. We intr

Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution

arXiv:2601.20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption. Current test

TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology

arXiv:2607.08803v2 Announce Type: replace-cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine underst

Value Drifts: Tracing Value Alignment During LLM Post-Training

model-releasesarxiv-cs-lg

arXiv:2510.26707v2 Announce Type: replace-cross Abstract: As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw

How Many Tasks Are Enough for Agent Benchmark Decisions? A Replay Analysis of Public LLM Agent Benchmarks

arXiv:2607.12338v1 Announce Type: new Abstract: Agent benchmarks often compare two agents after all tasks have run, but costly evaluations make partial runs tempting. A task fraction alone does not sh

15 Jul 2026

Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents

arXiv:2607.12790v1 Announce Type: new Abstract: Self-evolving agent systems improve by creating, revising, and retiring their own skills, but every such loop rests on a hidden assumption: a reliable e

15 Jul 2026

Local Ai

Bonsai 27B: 1-bit dense LLM running locally in your browser using custom WebGPU kernels

Very impressive release by the PrismML team. 1-bit quantization shrinks it from 54GB to just 3.8GB (-93%), while retaining 90% of its intelligence. - Collection on Hugging Face: https://huggingface.co

local-air-localllama

14 Jul 2026

Hardware

AI Model Co-Design: Hardware-Friendly LLM Design

AI model co-design involves integrating model requirements into hardware planning to ensure memory hierarchies and networking stacks are purpose-built for specific systems . AI performance balances th

hardwarenvidia-developer

HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning

arXiv:2601.22448v2 Announce Type: replace-cross Abstract: RLVR has become a standard recipe for training LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cos

safetyarxiv-cs-cl

KronQ: LLM Quantization via Kronecker-Factored Hessian

model-releasesarxiv-cs-lg

arXiv:2607.07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining. Existing second-order PT

ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning

arXiv:2607.07719v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-ran

Hardware

Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading

Large language model training workloads increasingly encounter GPU memory limits before compute is fully utilized, with high-bandwidth memory capacity becoming the primary scaling bottleneck as model

hardwarenvidia-developer

Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance

arXiv:2607.08602v1 Announce Type: new Abstract: Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality. Current guidelines and staging systems provide co

Two Axes of LLM Abstention: Answer Correctness and Question Answerability

arXiv:2607.08456v1 Announce Type: cross Abstract: A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones r

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

arXiv:2607.08046v1 Announce Type: cross Abstract: Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully

When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning

arXiv:2607.07976v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely u

A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving

model-releasesarxiv-cs-lg

arXiv:2607.07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic

Ego-Human Motion Prediction with 3D-Aware LLM

model-releasesarxiv-cs-cv

arXiv:2607.07001v1 Announce Type: new Abstract: Anticipating human motion from an egocentric perspective is fundamental for proactive assistance in AR/VR, human-robot collaboration, and embodied AI. W

FPTQuant: Function-Preserving Transforms for LLM Quantization

arXiv:2506.04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective

researcharxiv-cs-lg

Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference

arXiv:2607.07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantizati

researcharxiv-cs-lg

LLM-powered reasoning in agent-based modeling

arXiv:2607.06757v1 Announce Type: new Abstract: Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs

Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs

arXiv:2510.25370v2 Announce Type: replace Abstract: Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Commu

researcharxiv-cs-cl

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning

model-releasesarxiv-cs-cl

arXiv:2607.07557v1 Announce Type: new Abstract: One-shot pruning methods like Wanda and SparseGPT apply the same sparsity ratio to every layer of a transformer, ignoring known variation in layer impor

Predicting LLM Safety Before Release by Simulating Deployment

arXiv:2607.07184v1 Announce Type: cross Abstract: Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about

Specification Grounding Drives Test Effectiveness for LLM Code

arXiv:2607.06636v1 Announce Type: cross Abstract: Large language models frequently generate code that appears correct on typical inputs yet fails on edge cases, invalid inputs, and other specification

Successor-Generator Planning with LLM-generated Heuristics

arXiv:2501.18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized

Applications

CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries

arXiv:2607.05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user's i

applicationsarxiv-cs-ai

8 Jul 2026

DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail

arXiv:2607.06326v1 Announce Type: new Abstract: Large language models deployed in open-world applications require safety guardrails that are both robust to complex risks and efficient enough for low-l