Search: “llm”

The Asymmetric Harms of LLM Compression

arXiv:2608.19670v1 Announce Type: new Abstract: Large language models (LLMs) compression reduces deployment costs, but standard aggregate metrics like perplexity and accuracy often mask underlying beh

21 Aug 2026

Agents

The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices

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DGX agent

arXiv:2509.02910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants. While the outsourcin

agentsarxiv-cs-ai

21 Aug 2026

World-Model-Grounded LLM Planning for AUV and ASV Navigation Near Offshore Wind Farms

model-releasesarxiv-cs-ro

arXiv:2608.19661v1 Announce Type: new Abstract: Large language models can turn a natural-language mission into a sequence of robot actions, but they do not have a sense of physics: they cannot judge h

21 Aug 2026

Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference

arXiv:2608.18591v1 Announce Type: new Abstract: Uniformly allocating inference reasoning budgets to LLMs is expensive and prone to over-thinking penalties; especially in document tasks where visual la

20 Aug 2026

Future Policy Approximation for Offline Reinforcement Learning in LLM Reasoning

arXiv:2509.19893v3 Announce Type: replace Abstract: Reinforcement learning (RL) has emerged as a key driver of post-training for complex reasoning in large language models (LLMs), yet online RL introd

20 Aug 2026

Decomposition Attacks Across Unlinkable Identities: Limits of Stateful Defenses for LLM Services

arXiv:2608.17445v1 Announce Type: cross Abstract: Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attacks exploi

Do LLMs Know a Good Hypothesis When They See One? Logit-Based Energy Scoring Outperforms Prompted LLM-as-Judge for Scientific Hypothesis Ranking

arXiv:2608.17270v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for scientific hypothesis generation. However, evaluating generated hypotheses remains a challenge fo

From Entity Mentions to Tone: An LLM-Based Pipeline for Media Bias Analysis

arXiv:2608.17454v1 Announce Type: new Abstract: This paper presents a pipeline for analyzing media bias and framing in online news. The pipeline groups articles into topics and events, adds named-enti

Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training

arXiv:2608.16927v1 Announce Type: cross Abstract: As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and i

researcharxiv-cs-cv

Tutorials

KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn

arXiv:2608.17150v1 Announce Type: new Abstract: To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content

tutorialsarxiv-cs-ai

TileMix: Tile-Centric Mixed-Precision Attention for LLM Inference Acceleration

arXiv:2608.17336v1 Announce Type: new Abstract: Long-context prefill in large language models (LLMs) incurs substantial computation and memory traffic because dense self-attention computes quadratic q

What Aggregate Scores Miss: Measuring Item-Level Regressions in Commercial LLM API Migrations

arXiv:2608.17719v1 Announce Type: cross Abstract: Context: Software systems that depend on commercial large language model APIs must migrate to successor versions when vendors deprecate older models.

When will there be a frontier level llm with updateable engrams or fixed engrams?

model-releasesr-localllama

DeepSeek released on a paper on pretrained engrams in January. I’m surprised ds didn’t release engrams with v4 pro. When will ds or another lab release a fixed engram model? Fixed engrams will be the

Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling

arXiv:2608.15565v1 Announce Type: new Abstract: Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known

Local Ai

Beyond Direct Access: Resource Hijacking in LLM Agents

arXiv:2608.15108v1 Announce Type: cross Abstract: Large language model agents are increasingly connected to high-value resources such as computing infrastructure, credentials, usage budgets, identitie

local-aiarxiv-cs-ai

Beyond the pale: Assessing prevalence and contents of extremist speech in LLM training data

arXiv:2608.14813v1 Announce Type: new Abstract: Despite a strong interest on the part of the research community in the topic of trustworthy and safe AI, the composition of the text corpora that large

researcharxiv-cs-cl

Local Ai

Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration

arXiv:2608.15772v1 Announce Type: new Abstract: When a language model refuses to answer a prompt, it is unclear whether the correct answer is erased from its internal representations, or merely suppre

local-aiarxiv-cs-ai

CAPO: Constraint-Aware Prompt Optimization for LLM Agents

arXiv:2608.16068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose

Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews

arXiv:2608.14737v1 Announce Type: cross Abstract: This study analyses LLMs in imbalanced binary classification, using study screening in systematic reviews as the application domain. An experiment was

CompoSkill: Compositional Skill Chain Attacks from Individually Scanner-Passing LLM Agent Skills

arXiv:2608.16246v1 Announce Type: cross Abstract: Autonomous AI agents tackling Long Horizon Tasks depend on marketplace skills that are certified one at a time: a scanner returns a safety verdict for

Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents

arXiv:2608.15109v1 Announce Type: new Abstract: Generating structurally valid synthetic tabular data remains difficult: outputs with high statistical fidelity and downstream utility can still violate

Hardware

FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy

arXiv:2608.15602v1 Announce Type: cross Abstract: While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlo

hardwarearxiv-cs-ai

I-CALM: Incentivizing Confidence-Aware Abstention for LLM Selective Answering

arXiv:2604.03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing ove

Le Critique: Privileged Value Functions for LLM Reinforcement Learning

arXiv:2608.16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative method

safetyarxiv-cs-lg

Mental Model Management: An Operator-Based Framework for LLM Memory

arXiv:2608.15451v1 Announce Type: new Abstract: Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conceptual repres

Agents

MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems

arXiv:2608.16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science wo

agentsarxiv-cs-ai

OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks

arXiv:2608.15436v1 Announce Type: new Abstract: Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource

PCA-guided Activation Scaling for Monotonic Bidirectional Control over LLM Sycophancy

arXiv:2608.16650v1 Announce Type: new Abstract: Large language models (LLMs) exhibit sycophancy, a tendency to agree with user beliefs regardless of factual accuracy. This can reinforce misconceptions

researcharxiv-cs-cl

PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data

arXiv:2608.16419v1 Announce Type: cross Abstract: Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here

Prior Audit-Repair Context Shifts LLM Verifier Thresholds Toward Leniency

arXiv:2608.16003v1 Announce Type: new Abstract: Automated checking pipelines increasingly place one language model as the checker and another (or the same one) as the fixer. We ask whether that wiring

SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization

model-releasesarxiv-cs-lg

arXiv:2608.15567v1 Announce Type: new Abstract: Weight-only post-training quantization (PTQ) enables the deployment of large language models under tight memory budgets, but accuracy often collapses at

STAGE: Controlled Objective Admission for Multi-Preference LLM Alignment

model-releasesarxiv-cs-cl

arXiv:2608.16553v1 Announce Type: new Abstract: Multi-preference alignment is often framed as scalarization: combine reward dimensions, then optimize. This leaves a temporal decision underspecified: w

SubZero+: Efficient Zeroth-Order LLM Fine-Tuning via Large Learning Rates

model-releasesarxiv-cs-lg

arXiv:2608.15665v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables backpropagation-free fine-tuning of large language models, but existing ZO methods suffer from high-variance grad

Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values

arXiv:2607.14345v4 Announce Type: replace-cross Abstract: People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: th

Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs

arXiv:2608.16391v1 Announce Type: cross Abstract: As large language models become increasingly widespread, third-party providers that deploy open-weight models have become an important part of the eco

WeSCE: A Benchmark for Measuring Security Drift in LLM-Driven Code Editing

arXiv:2608.15092v1 Announce Type: cross Abstract: In this work, we introduce WeSCE, a benchmark for quantifying security drift in code editing under weak-security constraints, where tasks specify only

Built a token-aware gateway/load balancer for local LLM stacks — because nginx has no idea what a token costs

If you're running Ollama, llama.cpp, or vLLM behind nginx or HAProxy for more than a single user, you've probably hit this: nginx treats a 10-token prompt and a 10k-token prompt as identical 'one requ

model-releasesr-ollama

Applications

Musical Mirrors: The LLM as Sounding Board in Songwriting

arXiv:2608.13944v1 Announce Type: cross Abstract: This paper examines a use of AI in creative practice as an interpretive sounding board for human-generated material, rather than the more familiar pat

applicationsarxiv-cs-ai

Tutorials

No Universal Signal Predicts Sample-Level LLM Regression under Version Updates

arXiv:2608.13607v1 Announce Type: new Abstract: Frontier LLMs are updated frequently and typically outperform their predecessors in aggregate. But aggregate gains say little about individual samples:

tutorialsarxiv-cs-ai

SemPlan: Benchmarking Structured Semantic Planning for LLM-Based Queries over Enterprise Data

arXiv:2608.13612v1 Announce Type: new Abstract: Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queri

Beyond the Best Guess: Improving LLM Solution Coverage with Evolution Strategies

arXiv:2608.12679v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science. The usual approach is to present the problem to th

From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks

arXiv:2608.12350v1 Announce Type: cross Abstract: The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cos

Hardware

Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference

arXiv:2608.13426v1 Announce Type: cross Abstract: Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplicati

hardwarearxiv-cs-ai

SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents

arXiv:2608.13173v1 Announce Type: new Abstract: Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing. Exist

Teach the Magnitude, Not the Direction: Verifier-Bounded Credit Assignment for Multi-Turn Multi-step LLM Agents

arXiv:2608.13179v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) offers a verifier-bounded performance ceiling for training multi-turn tool-use agents, yet its tra

A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation

arXiv:2512.06547v4 Announce Type: replace-cross Abstract: Decoupled PPO has been a successful reinforcement learning (RL) algorithm to deal with the high data staleness under the asynchronous RL setti

13 Aug 2026

Agents

Credo: Declarative Control of LLM Pipelines via Beliefs and Policies

arXiv:2604.14401v2 Announce Type: replace Abstract: Agentic AI systems are becoming commonplace in domains that require long-lived, stateful decision-making in continuously evolving conditions. As suc

agentsarxiv-cs-ai

13 Aug 2026

Hardware

Cutting AI Datacenter Energy with Reinforcement Learning: Measured Power Control of LLM Training from One GPU to the Fleet