Search: “llm”

LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

arXiv:2508.17692v2 Announce Type: replace Abstract: Recent advances in LLM-based agents highlight the importance of their reasoning frameworks, which guide the problem-solving process in diverse ways.

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PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents

DGX agent

arXiv:2610.01349v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents turn generated text into real side effects, so poisoned tool metadata, retrieved pages, memory, and reusa

model-releasesarxiv-cs-ai

2 Oct 2026

The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization

arXiv:2610.00983v1 Announce Type: cross Abstract: Post-training quantization (PTQ) methods typically use sequential quantization that partitions a pre-trained LLM into a series of units (e.g., transfo

WaLLM -- Understanding Use and Engagement with a General-Purpose LLM on WhatsApp

arXiv:2505.08894v2 Announce Type: replace-cross Abstract: Large language model (LLM) chatbots are increasingly reaching users through messaging platforms (e.g. WhatsApp). However, these systems remain

When Do Causal World Models Help Modular LLM Agents

arXiv:2610.00012v1 Announce Type: new Abstract: LLM agents increasingly act through modular systems, such as order, payment, inventory, and shipment services, where actions in one module change which

Explicit Trajectory Diversity for RL-Based Post-Training of LLM Agents

arXiv:2609.38805v1 Announce Type: cross Abstract: LLM agents often admit multiple high-quality solutions to the same task, differing in reasoning structure, tool-use pattern, or interaction trajectory

Explore-on-Graph: Hybrid Embedding-LLM Reasoning for Knowledge Graph Question Answering under Incompleteness

model-releasesarxiv-cs-cl

arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based K

Hardware

SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs

arXiv:2609.38830v1 Announce Type: new Abstract: Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution behavior

hardwarearxiv-cs-lg

Whose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening

arXiv:2609.38818v1 Announce Type: new Abstract: Organizations increasingly route employee feedback to leaders through large language model (LLM) summaries, an unaudited layer that silences already-spo

Backdoor Mitigation in Decentralized LLM Fine-Tuning

arXiv:2609.37367v1 Announce Type: cross Abstract: Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a central

researcharxiv-cs-lg

30 Sept 2026

How to Run Statistics over LLM Judges and Trust the Results: Calibrated Inference for Small-Sample AI Evaluation with evalstats

arXiv:2609.35815v1 Announce Type: cross Abstract: Researchers across academia increasingly base significance claims on LLM judge scores and small-sample AI evaluations. Yet without well-calibrated con

30 Sept 2026

Easier Said Than Done: Unpacking Intent-Behavior Gap in Jailbreaking LLM-based Robots

arXiv:2412.16633v4 Announce Type: replace Abstract: LLM-based robots use Large Language Models (LLMs) as planners to translate natural language instructions into policies such as grasp(), move_to(), a

safetyarxiv-cs-ro

29 Sept 2026

DynBranch: Speculative Subgraph Reuse for Dynamic Agentic LLM Serving

arXiv:2609.31047v1 Announce Type: cross Abstract: Agentic LLM workflows decide their execution paths at runtime. Downstream computation may be predictable, or may have run before, yet it cannot begin

28 Sept 2026

Bridging LLM Serving and CXL-SSDs with Chunk-Aware KV Cache Management

arXiv:2609.26828v1 Announce Type: cross Abstract: NAND-backed storage offers the capacity needed to scale LLM prefix caching, but its block I/O path incurs CPU cache contention and host-DRAM staging i

24 Sept 2026

Validation and Simulation Catch Different Errors: Four Levels of Evaluation for LLM-Generated Circuits

arXiv:2609.26830v1 Announce Type: cross Abstract: Simulation success is not equivalent to structural correctness for LLM-generated circuits. We define and measure four evaluation levels -- schema vali

24 Sept 2026

Djinnlang: Higher-Level Programming by Unambiguous Specification with an LLM in the Compiler

arXiv:2609.23954v1 Announce Type: cross Abstract: Programmers write formal specifications, and LLMs implement them, proving that each implementation matches its spec. Taken to its extreme, this makes

23 Sept 2026

Towards Proactive Detection of User-Side Implicit Conflicts in Human-LLM Dialogue

model-releasesarxiv-cs-cl

arXiv:2609.19155v1 Announce Type: new Abstract: In Human-LLM dialogue, follow-up user utterances may implicitly conflict with earlier intents, leading the LLM to misinterpret user needs and generate i

18 Sept 2026

ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions

arXiv:2609.18864v1 Announce Type: cross Abstract: Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss unaut

17 Sept 2026

Tutorials

Rollback the World, Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents

arXiv:2609.18304v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can a

tutorialsarxiv-cs-cl

17 Sept 2026

Who Judges Matters: Measuring Family-Conditioned Preference in LLM-as-Judge Panels

arXiv:2609.17857v1 Announce Type: cross Abstract: Who the judge is can affect an LLM-as-judge result, but measuring that effect without confusing it with candidate quality is difficult. We study four

17 Sept 2026

Interpreting and Steering LLM Agents for Social Simulations

arXiv:2609.16436v1 Announce Type: cross Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the s

16 Sept 2026

Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control

arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provide

Evaluating LLM-Generated Rules for Heart Disease Prediction

model-releasesarxiv-cs-lg

arXiv:2609.13192v1 Announce Type: new Abstract: This study compares traditional machine learning models and Large Language Model (LLM)-generated rule-based systems for heart disease prediction using t

Four Ledgers, Not One Score: Responsible Communication of LLM-Judge Calibration in Biomedical ML

arXiv:2609.15015v1 Announce Type: new Abstract: Synthetic perturbations appear to offer inexpensive calibration data for LLM evaluators in biomedical ML, where expert review is scarce. Yet a planted m

The Router Within: Eliciting Native Skill Routing from a Frozen LLM

arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by prel

Can We Trust LLM Judges: A Study of Capability-Dependent Biases and Multi-Judge Ensemble for Bias Calibration

arXiv:2609.12002v1 Announce Type: new Abstract: LLMs are increasingly used as automated judges for model training and evaluation, yet individual judges exhibit systematic biases that undermine reliabi

safetyarxiv-cs-lg

14 Sept 2026

Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding

arXiv:2609.12243v1 Announce Type: new Abstract: Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-tra

researcharxiv-cs-cl

14 Sept 2026

Hardware

Phase-Decoupled, Model-Calibrated Power Control for Disaggregated LLM Serving

arXiv:2609.11133v1 Announce Type: new Abstract: Datacenter GPU power is the binding constraint on LLM serving capacity, and production serving has shifted to prefill/decode (PD) disaggregation. Deploy

hardwarearxiv-cs-lg

11 Sept 2026

When Noise Fabricates Bias: The Fragility of LLM-as-a-Judge Bias Measurement under Noisy Text

arXiv:2609.11067v1 Announce Type: new Abstract: Large language models are increasingly used as judges to measure social bias in text, yet the passages they judge are often noisy, containing typos, inf

safetyarxiv-cs-cl

11 Sept 2026

EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph

arXiv:2609.05553v1 Announce Type: new Abstract: Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summar

10 Sept 2026

Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation

arXiv:2609.08599v1 Announce Type: new Abstract: Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across

10 Sept 2026

Measuring LLM performance drift: observations and methodology from 31,352 repeated benchmark measurements [D]

model-releasesr-machinelearning

One thing that has bothered me about LLM benchmarks for a while is that most of them are essentially snapshots. A model is evaluated, a score is published, and we tend to talk about that score as if i

7 Sept 2026

Decoupled Analysis-Judging: An Automated Creativity Evaluator Using LLMs in Complex Multi-step Creativity Tasks

arXiv:2609.03432v1 Announce Type: new Abstract: Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to biases such as verbosity bias and leniency bia

safetyarxiv-cs-cl

4 Sept 2026

LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency

arXiv:2604.05460v2 Announce Type: replace-cross Abstract: Large language model (LLM) evaluation platforms increasingly rely on pairwise human judgments. These data are noisy, sparse, and non-uniform,

4 Sept 2026

Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents

arXiv:2609.02042v1 Announce Type: new Abstract: Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol, issuing one primitive action per LLM round

safetyarxiv-cs-lg

3 Sept 2026

EDGE: Error Dependency Graph-Guided Multi-Error Attribution in Multi-Agent LLM Systems

arXiv:2609.01360v1 Announce Type: new Abstract: Large language model (LLM) agent failures often contain multiple related errors rather than a single mistake. Existing attribution methods usually ident

2 Sept 2026

Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response

arXiv:2511.15755v3 Announce Type: replace Abstract: Large language models (LLMs) promise to accelerate incident response in production systems, yet single-agent approaches generate vague, unusable rec

2 Sept 2026

mzCache: On-Device LLM Memory Management under Multitasking

model-releasesarxiv-cs-lg

arXiv:2609.01338v1 Announce Type: cross Abstract: On-device mobile Large Language Model (LLM) inference is gaining significant attention. However, mobile devices operate in highly dynamic multitasking

2 Sept 2026

Evaluating and Improving LLM Self-Modeling

arXiv:2608.30980v1 Announce Type: cross Abstract: We study self-modeling: an LLM's ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a pr

FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validation

arXiv:2608.30192v1 Announce Type: new Abstract: Traditional finance relies on experts to hand-craft factors through a principled process grounded in economic rationale. Recent LLM-based multi-agent sy

KV Admission: Learning What to Write for Efficient Long-Context LLM Inference

arXiv:2512.17452v4 Announce Type: replace-cross Abstract: Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear Key-Value (KV) cache growth. Prior approaches miti

On the Prospects of Dynamic LLM Conversations in Software Development

arXiv:2608.30756v1 Announce Type: cross Abstract: Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their

Pak3H: Evaluating the Cost of Cultural Mismatch in LLM Alignment with a Human-Contextualized Urdu Benchmark

arXiv:2608.30065v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate strong Helpfulness, Harmlessness, and Honesty (3H) alignment in English-centric settings, but these gains tra

Hardware

PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response

arXiv:2608.21719v1 Announce Type: cross Abstract: AI inference clusters are increasingly constrained by instantaneous power, not just energy: grid operators condition new capacity on demand response,

hardwarearxiv-cs-ai

Repair or Resample? Rethinking Failure Debugging in LLM Multi-Agent Systems

arXiv:2608.25920v2 Announce Type: replace Abstract: As large language model (LLM)-based multi-agent systems (MASs) are increasingly applied to long-horizon complex tasks, their reliability has emerged

Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms

model-releasesarxiv-cs-lg

arXiv:2608.28044v1 Announce Type: cross Abstract: Large language model (LLM) inference serving is priced by tokens, but GPU energy is consumed over inference windows. This accounting mismatch makes to

31 Aug 2026

// ContextLeak in AI Agents // The whole attack surface here is a tool name and a tool description. Stealing an LLM agent's runtime context,…

// ContextLeak in AI Agents // The whole attack surface here is a tool name and a tool description. Stealing an LLM agent's runtime context, meaning the user prompt, the execution trajectory and the t

agentsdair-ai--x

31 Aug 2026

Approved Too Late: Verdict Staleness in LLM-Guarded Self-Adaptive Systems

arXiv:2608.26306v1 Announce Type: new Abstract: A large language model (LLM) guardrail for a self-adaptive system (SAS) may issue an approval that is correct at check time but stale by actuation. This