Search: “llm”

GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)

arXiv:2604.17091v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are fundamentally limited by context. As interactions become longer, tool descriptions, retrieved memorie

agentsarxiv-cs-cl

21 Apr 2026

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Local Ai

GraSP: Graph-Structured Skill Compositions for LLM Agents

DGX agent

arXiv:2604.17870v1 Announce Type: new Abstract: Skill ecosystems for LLM agents have matured rapidly, yet recent benchmarks show that providing agents with more skills does not monotonically improve p

local-aiarxiv-cs-cl

21 Apr 2026

Applications

Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance

arXiv:2604.17650v1 Announce Type: new Abstract: LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, p

applicationsarxiv-cs-cl

21 Apr 2026

Tools

llm-openrouter 0.6

Release: llm-openrouter 0.6 llm openrouter refresh command for refreshing the list of available models without waiting for the cache to expire. I added this feature so I could try Kimi 2.6 on OpenRout

toolssimon-willison

20 Apr 2026

LLM Reasoning Is Latent, Not the Chain of Thought

arXiv:2604.15726v1 Announce Type: new Abstract: This position paper argues that large language model (LLM) reasoning should be studied as latent-state trajectory formation rather than as faithful surf

researcharxiv-cs-ai

20 Apr 2026

Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents

arXiv:2604.05808v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex interactive decision-making tasks. However, existing LLM ag

researcharxiv-cs-lg

16 Apr 2026

Tutorials

Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies

arXiv:2604.14111v1 Announce Type: new Abstract: Large Language Models (LLMs) are now capable of generating highly fluent, human-like text. They enable many applications, but also raise concerns such a

tutorialsarxiv-cs-cl

16 Apr 2026

Hardware

StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving

arXiv:2604.09562v1 Announce Type: cross Abstract: Efficient LLM serving must balance throughput and latency across diverse, bursty workloads. We introduce StreamServe, a disaggregated prefill decode s

hardwarearxiv-cs-ai

14 Apr 2026

llm-server v2 ai-tuning it self now best performance for llama.cpp/ik_llama.cpp (big steps form v1) auto flag optimization

**llm-server v2** is a community-developed local LLM server tool that introduces AI-driven self-tuning capabilities, automatically identifying and applying optimal performance flags for **llama.cpp**

model-releasesr-ollama

11 Apr 2026

Blending Human and LLM Expertise to Detect Hallucinations and Omissions in Mental Health Chatbot Responses

arXiv:2604.06216v1 Announce Type: cross Abstract: As LLM-powered chatbots are increasingly deployed in mental health services, detecting hallucinations and omissions has become critical for user safet

Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

model-releasesarxiv-cs-cl

arXiv:2604.08369v1 Announce Type: cross Abstract: Inference-time compute scaling has emerged as a powerful technique for improving the reliability of large language model (LLM) agents, but existing me

MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors

arXiv:2604.06846v1 Announce Type: cross Abstract: Interactive medical dialogue benchmarks have shown that LLM diagnostic accuracy degrades significantly when interacting with non-cooperative patients,

On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins

arXiv:2603.25898v2 Announce Type: replace-cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and senso

Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents

arXiv:2610.18052v1 Announce Type: new Abstract: Long-term memory has become a core component of large language model (LLM) agents. As agents accumulate personal information over long-term use, users i

Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling

arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequenc

researcharxiv-cs-ai

Local Ai

SchemaWeaver: Accelerating On-Device Mobile Agentic AI via Schema-Guided Parallel LLM Decoding

arXiv:2610.17436v1 Announce Type: new Abstract: Large language models (LLMs) on mobile devices can act as agents that call external software tools to complete complex tasks, but the major challenge of

local-aiarxiv-cs-ai

When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems

arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to o

Insightful paper on recursive self-improvement with a frozen LLM. (bookmark it) Memento 3 clears every level of all 25 public ARC-AGI-3 game…

Insightful paper on recursive self-improvement with a frozen LLM. (bookmark it) Memento 3 clears every level of all 25 public ARC-AGI-3 games and uses 44% of the human action count. The underlying LLM

agentsdair-ai--x

14 Oct 2026

LACRM: LLM-Guided Mining of Directed Advertiser Competition Relationships in E-Commerce Advertising

model-releasesarxiv-cs-lg

arXiv:2610.13959v1 Announce Type: new Abstract: In e-commerce advertising, ad platforms typically fail to model the externalities among advertisers when making optimization decisions: an advertiser's

14 Oct 2026

Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents

arXiv:2610.13267v1 Announce Type: new Abstract: Large language model (LLM) agents are highly vulnerable to memory-poisoning attacks. Existing defenses primarily rely on external modules for either sta

12 Oct 2026

A Dual-Hypothesis Reasoning Framework for LLM Guardrails

arXiv:2607.17575v3 Announce Type: replace Abstract: We propose ARBITER, a novel LLM guardrail framework that introduces two key ideas: (i) dual-hypothesis reasoning, a reasoning method for LLM guardra

Tutorials

Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?

arXiv:2610.11135v1 Announce Type: new Abstract: Knowledge tracing (KT) models need many logged learners, so a new course or platform starts without a usable model. In LLM-based KT the LLM generates th

tutorialsarxiv-cs-cl

Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper

arXiv:2610.11542v1 Announce Type: cross Abstract: Multi-agent LLM systems negotiating with a stateful counterpart waste model calls in three ways: polite loops that never meet the counterpart's hidden

Prior or Feedback? What an LLM Uses When Adapting Neural Operators

arXiv:2610.12325v1 Announce Type: new Abstract: Do LLM scientific agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback? We study this q

APEX: Active Protection at Execution Boundaries for LLM Agents

arXiv:2610.06966v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime. As agents compose he

DecepEval: A Benchmark for Evaluating Deception in LLM Agents

model-releasesarxiv-cs-lg

arXiv:2610.07967v1 Announce Type: new Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their re

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

arXiv:2610.08215v1 Announce Type: new Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for

SpeedrunBench: Challenging LLM Agents with Video Game Speedrunning

arXiv:2610.08076v1 Announce Type: new Abstract: Frontier LLM agents have been shown to be capable of solving increasingly complex tasks for which humans have measurable solutions. This begs the pertin

VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two re

safetyarxiv-cs-lg

VietBinoculars: A Zero-Shot Approach for Detecting Vietnamese LLM-Generated Text

arXiv:2509.26189v2 Announce Type: replace Abstract: The rapid proliferation of Large Language Models has intensified the challenge of distinguishing LLM-generated text from human writing in non-Englis

researcharxiv-cs-cl

Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts

model-releasesarxiv-cs-lg

arXiv:2610.05690v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for literature search and synthesis. However, it is unclear whether they retrieve accurate bibliogr

6 Oct 2026

Applications

Jumping the Line: Exploiting Length Predictions in LLM Scheduling

arXiv:2610.03430v1 Announce Type: new Abstract: Efficient request scheduling is increasingly important for reducing completion time in large language model (LLM) serving. Size-based policies such as S

applicationsarxiv-cs-ai

5 Oct 2026

CARM: Cancellation-Aware Response Masking for LLM Reinforcement Learning

arXiv:2610.02039v1 Announce Type: cross Abstract: Recent years have witnessed the rapid adoption of reinforcement learning (RL) in large language model (LLM) post-training, with substantial gains in m

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

arXiv:2606.15396v2 Announce Type: replace-cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety g

Tutorials

Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget

arXiv:2610.01236v1 Announce Type: new Abstract: Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models wi

tutorialsarxiv-cs-ai

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.

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

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

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

researcharxiv-cs-ai

Local Ai

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

local-aiarxiv-cs-ai

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