Search: “llm”

Six misconceptions about large language models: A minimal model and diagnostic taxonomy

arXiv:2608.20421v1 Announce Type: cross Abstract: Large language models (LLMs) are now embedded in scientific, educational, and governance workflows, with debates centering on their capabilities, mech

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Sparse Token Routing in Efficient Transformers

DGX agent

arXiv:2608.20632v1 Announce Type: new Abstract: Efficient-transformer research often motivates token pruning and adaptive computation with the claim that not all tokens require equal computational eff

model-releasesarxiv-cs-cl

24 Aug 2026

Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding

arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive super

local-aiarxiv-cs-cv

Temporal Validity on Real Software Histories: Eliminating Stale-Fact Errors in Code-Assistant Memory over GitHub Fixes

arXiv:2608.20685v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has no model of time: when a fact changes across a coding session - a function is renamed, an endpoint moves, a d

researcharxiv-cs-ai

The Intrinsic Dimension of Prompts in Internal Representations of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2501.10573v2 Announce Type: replace Abstract: We study the geometry of token representations at the prompt level in large language models through the lens of intrinsic dimension. Viewing transfo

Toward Auto-Research: Mining Falsifiable Research Ideas from Paper Knowledge Graphs with Categorical Structure

arXiv:2608.20361v1 Announce Type: cross Abstract: Automated research-idea generation systems built on large language models (LLMs) share a structural weakness: they reduce ideation to free-text recomb

researcharxiv-cs-ai

TriPLU: Bypassing the Gate with Direct Trilinear Product FFNs in Tiny Language Models

arXiv:2608.20360v1 Announce Type: new Abstract: We study whether tiny decoder-only language models benefit from feed-forward layers that directly multiply learned feature projections. TriPLU, a Trilin

researcharxiv-cs-cl

Truth Lies Deep: Countering Semantic Camouflage via Latent Intent Verification

arXiv:2608.20378v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) is often superficial, relying on refusal mechanisms that trigger only at the final stages of generation

When Retrieval Fails Before It Begins: Structurally Indirect Prerequisite Eviction as a Retention Failure in Agentic Memory

arXiv:2608.20400v1 Announce Type: new Abstract: Agentic memory under a fixed budget involves two stages: retention and retrieval. Existing retrieval-centered paradigms implicitly assume necessary evid

When Vocabulary Comprehension Fails Clinical Reasoning: Evaluating Therapy Bots' Safety Risks for Generation Alpha

arXiv:2608.20345v1 Announce Type: cross Abstract: Conversational AI systems have become informal mental health support resources for Generation Alpha (Gen Alpha, born 2010-2024), with 13.1% of U.S. ad

Why2Speak: Faithful Reasoning for Abstaining Action Policies

arXiv:2608.20670v1 Announce Type: new Abstract: Many agentic systems must repeatedly choose between acting and abstaining, making faithful reasoning important for oversight: an explanation is useful o

World models of environment, agent and joint agent-environment systems

arXiv:2608.20401v1 Announce Type: new Abstract: World models are a central component of model-based reinforcement learning. They are usually discussed in terms of what variables they predict, such as

A Locally Tokenized Generative Model for Robust Time-Series Watermarking

arXiv:2608.19727v1 Announce Type: cross Abstract: Watermarking is a central tool for provenance in generative models, yet its application to multivariate time series remains hindered by reliability fa

local-aiarxiv-cs-ai

Automated Summarization of Financial News Using Large Language Models and Retrieval-Augmented Generation: An Early Empirical Study (Fall 2023)

arXiv:2608.19526v1 Announce Type: cross Abstract: Stock market analysts and investors face a daily challenge: too much financial news, too little time. Manually reading and synthesizing hundreds of co

applicationsarxiv-cs-ai

Beyond Imitation: Filtering On-Policy Distillation by Reasoning Progress

arXiv:2608.19408v1 Announce Type: new Abstract: On-policy distillation (OPD) has emerged as an effective framework for post-training language models by pairing student-generated trajectories with dens

Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration

arXiv:2608.19701v1 Announce Type: new Abstract: Long-term multi-agent systems continuously accumulate the memories produced by different agents. Existing memory methods typically treat retrieved memor

Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis

arXiv:2608.19902v1 Announce Type: new Abstract: AI agents can execute scientific analyses, but an analytic output becomes a defensible claim only after alternatives are weighed and the claim is limite

DARS: Dual-Level Credit Assignment RL with Structured Reasoning for Instruction-Based Image Editing

arXiv:2608.20161v1 Announce Type: new Abstract: Instruction-based image editing uses a planner-renderer pipeline: a vision-language model (VLM) first converts the instruction into an edit plan, and a

local-aiarxiv-cs-ai

Does Listening Matter? Backchanneling and Nodding in AI Clone

arXiv:2608.19527v1 Announce Type: cross Abstract: AI clones that imitate a specific person typically reproduce what the person says and how they sound, but not how they listen. We investigate whether

researcharxiv-cs-cl

Evidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Pools

arXiv:2608.19888v1 Announce Type: cross Abstract: Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn a new one,

FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills

arXiv:2607.21596v2 Announce Type: replace Abstract: Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usu

FormalTCS: Benchmarking End-to-End Frontier Formal Theoretical Computer Science Research of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2608.20153v1 Announce Type: new Abstract: Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far

Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses

arXiv:2608.19206v1 Announce Type: cross Abstract: Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creat

Improved Confidence Estimates for Black-Box Large Language Models

arXiv:2608.19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized confidence to

applicationsarxiv-cs-ai

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

arXiv:2608.19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer langua

safetyarxiv-cs-cl

Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation

arXiv:2608.20256v1 Announce Type: new Abstract: Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adaptive one, which

LoRA-GA^2: Low Rank Adaptation with Multi-step Gradient Adaptive Alignment

arXiv:2608.19800v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) is a prominent fine-tuning method for large models, achieving competitive performance with reduced memory overhead. However

OenoBench: A Wine-Domain Benchmark for Knowledge-Grounded Evaluation of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2608.20106v1 Announce Type: new Abstract: We introduce OenoBench, a wine-domain knowledge benchmark of 3,266 multiple-choice questions across six pillars (regions, grape varieties, viticulture,

Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training

arXiv:2608.19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes. Existing

local-aiarxiv-cs-ai

PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty

arXiv:2606.15654v2 Announce Type: replace-cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Obser

applicationsarxiv-cs-ai

Quantifying Event Impacts on Time Series via Multiscale Contrastive Learning

arXiv:2608.19447v1 Announce Type: new Abstract: Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal

applicationsarxiv-cs-lg

Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review

arXiv:2608.19214v1 Announce Type: new Abstract: The berth allocation and quay crane assignment problem (BACAP) is a representative port-terminal scheduling problem in maritime transportation and freig

Separating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition

arXiv:2608.19885v1 Announce Type: cross Abstract: Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn a new one,

When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Models

arXiv:2608.19230v1 Announce Type: cross Abstract: As language models move from drafting prose to running literature-search agents with tool calls, fabricated references are becoming easier to catch an

A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design

arXiv:2608.18279v1 Announce Type: cross Abstract: Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design

agentsarxiv-cs-lg

A Theory of Post-hoc Debate Judgement

arXiv:2608.19002v1 Announce Type: new Abstract: Debates have recently emerged as a useful methodology for agentic AI to improve performance as well as to aid explainability and user engagement. For ex

Tutorials

AI Can Learn Scientific Taste

arXiv:2603.14473v3 Announce Type: replace Abstract: Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with

tutorialsarxiv-cs-cl

Assessing Quality of Experience in Natural Language Generation of German Text

arXiv:2608.18888v1 Announce Type: new Abstract: The rapid advancement of Natural Language Generation (NLG) has made the reliable evaluation of generated text increasingly critical, as these systems, s

applicationsarxiv-cs-cl

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

arXiv:2608.18723v1 Announce Type: cross Abstract: Telecom operators traditionally offer predefined tariff grids, forcing users to choose from a limited set of plans. This paper proposes BFTR (Budget-F

Comment-level Topic Drift Analysis in the Reddit Corpus

arXiv:2608.19133v1 Announce Type: new Abstract: We present a novel application of embedding-based dynamic topic modeling techniques to detect and quantify topic drift at the comment level in a massive

researcharxiv-cs-cl

Competence, Not Accuracy: A Diagnostic for Reference-Free Judge Gates in Skill Optimization

arXiv:2608.18719v1 Announce Type: new Abstract: Text-space skill optimization adapts a frozen agent by evolving a natural-language skill document, accepting each candidate through a validation gate. E

Compiler-Guided Adaptive Proof Search with Cross-Model Synergy on Context-Dependent Theorem Proving

arXiv:2608.18084v1 Announce Type: new Abstract: Theorem proving in real-world Lean 4 projects is challenging because proofs often depend on project-specific context. While iterative refinement can use

applicationsarxiv-cs-cl

Computational Measurement of Team-Process Phase Dynamics in Collaborative Virtual Reality

model-releasesarxiv-cs-lg

arXiv:2608.18660v1 Announce Type: new Abstract: Collaborative virtual reality (VR) environments make team communication observable as it unfolds, but conventional transcript analyses often summarize e

ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety

arXiv:2508.20468v2 Announce Type: replace Abstract: Conspiracy theories erode public trust in science and institutions while resisting debunking by evolving and absorbing counter-evidence. As AI-gener

safetyarxiv-cs-cl

Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline

arXiv:2608.18911v1 Announce Type: new Abstract: We introduce the CDSP (context-conditional deliberation signal pipeline), converting an investment committee's meeting transcripts into structured predi

researcharxiv-cs-lg

ERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems

arXiv:2608.18469v1 Announce Type: cross Abstract: Lightweight proxy models enable rapid experimentation without repeatedly training frontier-scale systems, but their small kernels often leave modern a

Grouping the Stochastic Machine: Precision, Not Capability, as the Frontier Metric for AI Systems

arXiv:2608.19140v1 Announce Type: new Abstract: Frontier language models are compared, marketed, and benchmarked on capability -- what their best or average output can achieve. I argue this measures t

Harness Continual Learning: Continual Adaptation Beyond Model Parameters

arXiv:2608.19013v1 Announce Type: cross Abstract: Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern agents can a