Search: “llm”

RL makes MLLMs see better than SFT

model-releasesarxiv-cs-cv

arXiv:2510.16333v2 Announce Type: replace Abstract: A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its imm

14 Apr 2026

Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets

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DGX agent

arXiv:2604.02460v2 Announce Type: replace Abstract: Recent work reports strong performance from multi-agent LLM systems (MAS), but these gains are often confounded by increased test-time computation.

model-releasesarxiv-cs-cl

14 Apr 2026

Agents

SkVM: Revisiting Language VM for Skills across Heterogenous LLMs and Harnesses

arXiv:2604.03088v3 Announce Type: replace-cross Abstract: LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current system

agentsarxiv-cs-lg

14 Apr 2026

Accelerating Local LLMs on Resource-Constrained Edge Devices via Distributed Prompt Caching

model-releasesarxiv-cs-lg

arXiv:2602.22812v2 Announce Type: replace Abstract: Since local LLM inference on resource-constrained edge devices imposes a severe performance bottleneck, this paper proposes distributed prompt cachi

Local Ai

Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models

arXiv:2505.12509v3 Announce Type: replace-cross Abstract: Post-hoc explanations provide transparency and are essential for guiding model optimization, such as prompt engineering and data sanitation. H

local-aiarxiv-cs-ai

SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

arXiv:2604.08477v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathem

researcharxiv-cs-cl

Verbalizing LLMs' assumptions to explain and control sycophancy

arXiv:2604.03058v2 Announce Type: replace-cross Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like 'am I in the wrong?' rather than providing genuine assessment.

safetyarxiv-cs-ai

Benchmark Your Local LLMs in 3 Commands

This r/ollama post describes a streamlined method for performance-testing locally-running large language models using the Ollama framework, achievable with just three terminal commands. It likely intr

model-releasesr-ollama

12 Apr 2026

Local Ai

@hwchase17 I think harness/managed agents is a way for Anthropic to keep its Moat. As models get mature, the need for cloud LLMs might reduc…

local-aiharrison-chase--x

@hwchase17 I think harness/managed agents is a way for Anthropic to keep its Moat. As models get mature, the need for cloud LLMs might reduce and local LLM models might increase (save cost) and thus t

12 Apr 2026

Tutorials

LLMs learn backwards, and the scaling hypothesis is bounded. [D]

tutorialsr-machinelearning

This r/MachineLearning discussion post argues that LLMs acquire knowledge in a counterintuitive 'backwards' order during training — learning complex, high-level patterns before simpler foundational on

12 Apr 2026

👇 “I still maintain that LLMs are really dumb and of limited use. it’s my opinion as a practitioner and professional that the hype being pe…

👇 “I still maintain that LLMs are really dumb and of limited use. it’s my opinion as a practitioner and professional that the hype being peddled by the AI corporations and self promoters (from X grift

safetygary-marcus--x

11 Apr 2026

Extracting Breast Cancer Phenotypes from Clinical Notes: Comparing LLMs with Classical Ontology Methods

arXiv:2604.06208v1 Announce Type: cross Abstract: A significant amount of data held in Oncology Electronic Medical Records (EMRs) is contained in unstructured provider notes -- including but not limit

researcharxiv-cs-ai

Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference

model-releasesarxiv-cs-cl

arXiv:2604.07394v1 Announce Type: cross Abstract: The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. W

SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization

arXiv:2511.11663v2 Announce Type: replace-cross Abstract: The emergence of accurate open large language models (LLMs) has sparked a push for advanced quantization techniques to enable efficient deploy

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training

arXiv:2604.07754v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve m

safetyarxiv-cs-cl

Agents

The Illusion of Stochasticity in LLMs

arXiv:2604.06543v1 Announce Type: cross Abstract: In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) operating

agentsarxiv-cs-lg

Tools

Stop trusting LLMs just because they're useful. 🛑 Our Head of DevRel @RoieSchwabco breaks down the two essential parts of a reliable AI sta…

Stop trusting LLMs just because they're useful. 🛑 Our Head of DevRel @RoieSchwabco breaks down the two essential parts of a reliable AI stack: ⚙️ Reasoning: What the LLM does (logic/synthesis/formatti

toolspinecone--x

9 Apr 2026

You don’t need that expensive of a build for local llms.

Been seeing a lot of people with their 64gb of ddr5 4x3090 or whatever strata builds and it’s been sorta depressing knowing i cant afford that but i finally finished my setup today so here we go, Qwen

18 Oct 2026

Can LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation

arXiv:2610.21468v1 Announce Type: cross Abstract: Every story has two orders: the order in which its events happen and the order in which they are told. A story is nonlinear when the two differ. We in

16 Oct 2026

Local Ai

Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization

arXiv:2610.22127v1 Announce Type: cross Abstract: Personalization of Large Language Models (LLMs) is increasingly important for user-centric applications, yet prior methods mainly adapt dense LLMs, wh

local-aiarxiv-cs-ai

16 Oct 2026

Exploring the Design Space of Permutation Diffusion LLMs

arXiv:2610.20635v1 Announce Type: cross Abstract: Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output po

researcharxiv-cs-ai

16 Oct 2026

AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs

arXiv:2610.16042v1 Announce Type: cross Abstract: The increasing use of artificial intelligence (AI) in mental health applications raises concerns about its potential to internalize and perpetuate soc

AuthGateBench: Benchmarking LLMs as Authorization Gate

arXiv:2610.16426v1 Announce Type: new Abstract: Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classi

Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs

arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langua

researcharxiv-cs-ai

GateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs

arXiv:2610.14754v1 Announce Type: new Abstract: Hybrid language models interleave Gated DeltaNet (GDN) layers, whose recurrent memory has a fixed size, with full-attention layers, whose key-value (KV)

How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs

model-releasesarxiv-cs-cl

arXiv:2610.19427v1 Announce Type: new Abstract: Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction. In legal prac

Tutorials

LLMs program using Reusable and Composable Abstract Representations

arXiv:2610.19238v1 Announce Type: new Abstract: When writing code, the same computation can be expressed in different programming languages and through different implementation strategies. We show tha

tutorialsarxiv-cs-lg

North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge

arXiv:2610.15282v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made substantial progress in multilingual question answering, yet their ability to understand low-resource socioling

Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning

arXiv:2610.10358v2 Announce Type: replace Abstract: As multimodal large language models (MLLMs) become more capable and widely deployed, concerns about privacy and safety have become increasingly pres

safetyarxiv-cs-ai

Agents

SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verification

arXiv:2610.15725v2 Announce Type: cross Abstract: Agent skills encode operational procedures and domain expertise as reusable knowledge assets, yet their distribution in plaintext exposes them to unau

agentsarxiv-cs-ai

What the Model Cannot See: Covert and Provable Text Invisibility in LLMs

arXiv:2610.15883v1 Announce Type: cross Abstract: Text wrapped in a trigger can be made behaviorally invisible to a language model: omitted from summaries and quotations, ignored in question answering

Words to World: Benchmarking LLMs in Interactive Physical Environments

arXiv:2610.15228v1 Announce Type: new Abstract: Words are increasingly becoming an interface for action: large language models (LLMs) can now invoke tools, operate devices, and make decisions that aff

I built BitRun: A standalone runner for 1-bit LLMs (100–127 t/s on Apple Silicon & 40–45 t/s on PC CPUs with zero compile)

I built BitRun because I wanted to run 1-bit ternary LLMs on ordinary laptops without melting my battery, spinning up a dedicated GPU, or wrestling with complex build environments. Right now, BitRun g

14 Oct 2026

LLM2R: From Large Language Models to 3D Reassemblers

arXiv:2610.18604v1 Announce Type: new Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because

researcharxiv-cs-cv

14 Oct 2026

Zeroth-Order Fine-Tuning of LLMs in Learnable Low-Rank Subspaces

model-releasesarxiv-cs-lg

arXiv:2610.15225v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables memory-efficient fine-tuning of large language models (LLMs). However, ZO optimization suffers from high variance

14 Oct 2026

AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs

arXiv:2610.13600v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on speech-text tasks, but standard audio encoders produce hundreds of tokens for even s

12 Oct 2026

DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs

arXiv:2610.13767v1 Announce Type: cross Abstract: Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains thes

12 Oct 2026

The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs

arXiv:2510.13154v3 Announce Type: replace Abstract: What happens inside a language model when alignment training conflicts with a cultural value it encodes? Across 16 MENA countries, 26 models, and 1.

safetyarxiv-cs-cl

12 Oct 2026

[vllm] Is it possible to run Qwen3.8-27B-FP8 on a 5090?

Based on my experiments and feedback from claude, it seems extremely unlikely and even if I get it working, not practical because of minimum context. But just asking here as a last effort. I'm able to

11 Oct 2026

Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes

arXiv:2610.11963v1 Announce Type: cross Abstract: A no-code fix resolves an invalid bug report by directing the user to change a setting, update to a version where the problem is already fixed, or adj

9 Oct 2026

Reading the Room: Foundations, Design, and Challenges of Normative Competence in LLMs

arXiv:2610.10906v1 Announce Type: new Abstract: Human communities are governed by normative systems: shared standards that produce extit{norms} dictating acceptable behavior, enforced through communit

safetyarxiv-cs-ai

9 Oct 2026

Java vllm-like framework claims 90% of perfomance of llama.cpp on local inference on NVIDIA GPUs by compiling Java to CUDA and cuTile

TornadoVM: The Java to CUDA engine: https://github.com/beehive-lab/TornadoVM jitLLM: The inference engine: https://github.com/beehive-lab/jitllm Deep dive talk: https://www.youtube.com/watch?v=HO5CpET

8 Oct 2026

When Rank Rises as LLMs Degrade

arXiv:2610.09647v1 Announce Type: cross Abstract: Post-training adapts language models in non-stationary environments. Practitioners monitor representation health with RankMe and related spectral stat

researcharxiv-cs-cl

8 Oct 2026

DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents

arXiv:2610.08102v1 Announce Type: new Abstract: Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management

How Well Do LLMs Reason with Noisy Evidence? An Active Visual Reasoning Benchmark

arXiv:2610.07751v1 Announce Type: new Abstract: Real-world reasoning rarely reduces to static question answering: agents must actively gather information from tools and sensors that are often noisy an

Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral Judgment

arXiv:2610.08670v1 Announce Type: cross Abstract: Language models increasingly act as agents. An agent that says an action is wrong and then takes it anyway is a different failure from one that does n

Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective

model-releasesarxiv-cs-cl

arXiv:2610.07894v1 Announce Type: new Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is insuf

SAE++: Cascaded Sparse Autoencoders Learn Multi-Level Visual Concepts in Multimodal LLMs

arXiv:2606.16193v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their internal visual representati