Search: “llm”

FastE: Readout-Triggered Token Compression for LLM Embedding Inference

arXiv:2609.08407v1 Announce Type: new Abstract: In this study, we identify depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qw

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From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles

DGX agent

arXiv:2609.07673v1 Announce Type: cross Abstract: Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a

local-aiarxiv-cs-ai

10 Sept 2026

Hardware

Interface-Aware KV Cache Quantization for Dense On-Chip NVM in Long-Context LLM Decoding

arXiv:2609.05764v1 Announce Type: cross Abstract: The key-value (KV) cache is the dominant memory bottleneck in long-context large language model (LLM) decoding: every step reads it entirely, so decod

hardwarearxiv-cs-ai

MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents

arXiv:2609.09115v1 Announce Type: new Abstract: Long horizon Large Language Model (LLM) agents rely on external memory systems to preserve user preferences and task knowledge across extended interacti

agentsarxiv-cs-ai

Hardware

Nice showcase that interesting LLM work can be done on single GPU!

hardwaresebastian-raschka--x

Nice showcase that interesting LLM work can be done on single GPU! I extended the GPT-2-style code from @rasbt's 'Build a Large Language Model (from Scratch)' so that it was a 6-expert (2 active) mixt

On the Context Sensitivity of LLM Moral Judgment

arXiv:2603.23114v2 Announce Type: replace Abstract: A human's moral decision depends heavily on the context. Yet research on LLM morality has largely studied fixed scenarios. We address this gap by in

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

arXiv:2609.09153v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions throug

local-aiarxiv-cs-ai

SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews

arXiv:2609.05505v1 Announce Type: new Abstract: Systematic reviews require sustained human judgment across thousands of records, yet existing evaluations of large language models (LLMs) typically exam

Spillover-Aware Multi-Value Steering for Pluralistic LLM Alignment

arXiv:2609.05800v1 Announce Type: new Abstract: Activation steering controls LLM behavior at inference time by adding learned directions to hidden states, but existing methods handle one concept at a

SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction

arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical s

Applications

Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning

arXiv:2609.06974v1 Announce Type: cross Abstract: Large language models achieve strong performance across diverse tasks, but deployment remains costly because of memory, latency, and energy demands. S

applicationsarxiv-cs-ai

Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agents

arXiv:2609.06815v1 Announce Type: cross Abstract: An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to ca

When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference

arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundation

When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability

arXiv:2609.07987v1 Announce Type: new Abstract: LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide litt

MARBO: Relational Belief Grounding for LLM Agents in Social Deduction Games

arXiv:2609.06563v1 Announce Type: cross Abstract: Social deduction games (SDGs) require agents to reason under partial observability by maintaining relational beliefs about hidden roles and team align

agentsarxiv-cs-lg

MetaKV: Adaptive KV Cache Compression for Constrained LLM Inference

arXiv:2609.07966v1 Announce Type: new Abstract: Key--value (KV) cache compression is an effective way to reduce the memory overhead of large language model (LLM) inference, particularly for long-conte

researcharxiv-cs-lg

Tutorials

Online Learning with LLM Experts from Limited Feedback

arXiv:2609.05820v1 Announce Type: new Abstract: We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We f

tutorialsarxiv-cs-lg

Q2D-Web uses agent citations, production web rankings, and a combined set expanded with LLM judgments. These three relevance sets reduce fal…

Q2D-Web uses agent citations, production web rankings, and a combined set expanded with LLM judgments. These three relevance sets reduce false negatives and reliance on a single labeling pipeline, whi

agentsperplexity--x

Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method

model-releasesarxiv-cs-lg

arXiv:2507.18073v2 Announce Type: replace Abstract: Deploying large language models (LLMs) is challenging due to their massive parameters and high computational costs. Ultra low-bit quantization can s

Structurally Close, Temporally Distant: Measuring Security Exposure in Long-Horizon LLM Agents

model-releasesarxiv-cs-lg

arXiv:2609.05911v1 Announce Type: cross Abstract: Long-horizon LLM agents interact with untrusted content, persistent memory, external state, and sensitive tools. Existing analyses often characterize

A Removal Based Approach to Improve LLM Faithfulness at Test-Time

arXiv:2609.04343v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for consequential decisions, making their explanations an important tool for auditing model behavior.

Compact-Memory LLM Agents via Online Max-Member Clustering and Atom-Aware Packing

arXiv:2609.04915v1 Announce Type: new Abstract: Many long-horizon LLM deployments face tight prompt budgets: latency, cost, and context limits make full-context prompting impractical as interaction le

CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls

arXiv:2609.05269v1 Announce Type: cross Abstract: LLM agent systems increasingly combine provenance tracking, authorization, policy enforcement, protocol adapters, and execution controls. However, ind

DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems

arXiv:2609.04749v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile,

Dynamic Adaptation of the LLM Context for Generating Routines with Coupled Semantics

arXiv:2609.04570v1 Announce Type: cross Abstract: LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one routine is defined by the runtime behavio

agentsarxiv-cs-ai

// Evaluating and Improving LLM Self-Modeling // Really interesting paper. Can a model answer questions about its own behavior? The question…

// Evaluating and Improving LLM Self-Modeling // Really interesting paper. Can a model answer questions about its own behavior? The questions are deliberately verifiable, such as whether a particular

model-releasesdair-ai--x

Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents

arXiv:2609.04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every

agentsarxiv-cs-ai

From Plausible to Actionable: A Position on LLM Self-Explanations

arXiv:2607.15957v2 Announce Type: replace Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as s

researcharxiv-cs-cl

It’s going to be so funny when a bridge collapses and hundreds die because it turns out an LLM did the design for the whole thing or even a …

It’s going to be so funny when a bridge collapses and hundreds die because it turns out an LLM did the design for the whole thing or even a part of it over a CAD application’s MCP server By funny I me

agentsgary-marcus--x

LLM-Assisted Behavioural and Scenario Augmentation for Agent-Based Energy Adoption Models

arXiv:2609.04866v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting struc

Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys

arXiv:2609.04382v1 Announce Type: cross Abstract: We present a systems-security case study of a two-node split-LLM training system whose privacy evaluation passed while leaving an observable channel u

local-aiarxiv-cs-lg

Reviewer Capability Governs Rejection Targeting, Not Repair Skill: Evidence from LLM Execute-Review-Revise Pipelines

arXiv:2609.04270v1 Announce Type: cross Abstract: Multi-agent LLM pipelines increasingly assign roles, including execution and verification, to models of different capability tiers. This is done becau

agentsarxiv-cs-cl

When LLM Decompilers Recompile More and Preserve Less

arXiv:2609.05370v1 Announce Type: cross Abstract: Decompilation recovers high-level source from compiled machine code and serves as a foundation for security tasks such as vulnerability detection and

I built an LLM benchmark harness that lets you browse and compare how models answered each question

model-releasesr-localllama

Hello everyone. I felt current LLM benchmark harnesses hand you headline numbers but offer no tooling to see how models actually answered each question (they dump everything to a JSONL or Parquet file

6 Sept 2026

“The expected value of a fully LLM-generated, non-human-reviewed patch is a net-negative by a considerable margin.”

model-releasesgary-marcus--x

“The expected value of a fully LLM-generated, non-human-reviewed patch is a net-negative by a considerable margin.” 🦔AI can only fix security vulnerabilities 26% of the time. Researchers at 1Password

6 Sept 2026

Otaku — an LLM frontend

model-releasesr-localllama

Otaku is an LLM frontend, primarily designed for roleplay, an alternative to SillyTavern and the like. However, It also works for general-purpose chat with local backends (including Ollama) or cloud m

5 Sept 2026

Counterfactual Fairness Audits of Multi-Step Clinical LLM Agents Require a Measured Per-Action Instability Floor

model-releasesarxiv-cs-cl

arXiv:2609.03221v1 Announce Type: new Abstract: Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically distinct but clinically identical patients diff

Evolving Excellence: Automated Optimization of LLM-based Agents

arXiv:2512.09108v2 Announce Type: replace-cross Abstract: Agentic AI systems built on large language models (LLMs) offer significant potential for automating complex workflows, from software developme

Not All LLM Workloads Are Equal: Benchmarking TPU Performance on Classification vs. Generation

model-releasesgoogle-cloud-ai

Moving Large Language Models (LLMs) from experimental prototypes into enterprise production exposes a critical truth: your infrastructure dictates both your performance ceilings and your unit economic

Where Does Harness-Optimization Value Live? Localized Gains and the Budget-Splitting Trap in Self-Evolving LLM Agents

arXiv:2609.02889v1 Announce Type: new Abstract: A growing body of work improves frozen large language models (LLMs) as agents by evolving their harness: the textual scaffolding around the model, inclu

local-aiarxiv-cs-cl

Anker unveils the Eufy MindBase, a local AI hub for its cameras that runs an on-device, Anker-developed LLM, with a dedicated AI chip and up to 48TB of storage (Jennifer Pattison Tuohy/The Verge)

Jennifer Pattison Tuohy / The Verge: Anker unveils the Eufy MindBase, a local AI hub for its cameras that runs an on-device, Anker-developed LLM, with a dedicated AI chip and up to 48TB of storage — T

local-aitechmeme

Tutorials

Compositional Spectral Prompts for LLM-based Online Time Series Forecasting

arXiv:2609.02093v1 Announce Type: new Abstract: To address the sequential and evolving nature of time series, the Online Time Series Forecasting (OTSF) task has been extensively studied in multiple do

tutorialsarxiv-cs-lg

Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight

arXiv:2609.01976v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such f

NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis

arXiv:2609.01971v1 Announce Type: new Abstract: AI is rapidly advancing neuroscience, yet many laboratories fail to fully unleash its potential due to significant interdisciplinary barriers. While pre

agentsarxiv-cs-cl

Prompt-Space Meta-Learning Does Not Transfer Across Users: A Frozen-LLM Negative Result

arXiv:2609.01615v1 Announce Type: new Abstract: Personalizing a frozen large language model (LLM) to individual users is often framed as a meta-learning problem in prompt space: each user is a task, a

safetyarxiv-cs-lg

Reviewing the Reviewer: LLM-Assisted Reviewer Feedback Generation for Guideline Compliance

arXiv:2602.10118v2 Announce Type: replace Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics, namely lazy thinking and non-specific critiques, has threatened rev

researcharxiv-cs-cl

Tools

sorry for the radio silence folks - got sucked into extreme LLM psychosis. but i can confidently say we have crossed over into a new age of …

sorry for the radio silence folks - got sucked into extreme LLM psychosis. but i can confidently say we have crossed over into a new age of AI Engineering and we are never, ever, looking back. this is

toolsswyx--x

Hardware

Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference