Search: “llm”

Chart2SVG: Editable SVG Generation from Raster Chart Images

arXiv:2608.26544v1 Announce Type: new Abstract: We present Chart2SVG, a multimodal large language model that converts static raster charts into structurally organized, semantically enriched SVGs that

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Compositional Generalization via Structural Identification in a Category-Theoretic Framework

DGX agent

arXiv:2608.26465v1 Announce Type: new Abstract: Compositional generalization is usually evaluated through model accuracy. We instead ask which structural or lexical identifications make held-out COGS

researcharxiv-cs-cl

28 Aug 2026

CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases

arXiv:2608.27391v1 Announce Type: new Abstract: LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to shar

Daydreaming: Stealing Hidden Agent Skills through Black-Box Task Interaction

arXiv:2608.26733v1 Announce Type: cross Abstract: Agent skills bundle instructions, reference data, and executable helpers that let a general agent perform specialized tasks. Hosted providers can keep

Tutorials

DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping

arXiv:2510.12979v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with multi-step reasoning and action generation abilities have shown promise in leveraging external tools to

tutorialsarxiv-cs-ai

Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference

arXiv:2608.26574v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through itera

Distributed Training using an Intelligent Network

arXiv:2608.26453v1 Announce Type: new Abstract: Distributed training across a wide area network (WAN) is challenging, as continuous parameter exchange by islands of compute is constrained by limited b

Evaluating Confidence-Gated Retrieval with Matched Trajectory Replay

arXiv:2608.26846v1 Announce Type: cross Abstract: Interactive language-model agents use confidence signals to decide whether to answer immediately, retrieve additional evidence (from memory or externa

Forward-Free Diffusion Language Models with BPTT-Free Looped Refinement

arXiv:2606.08357v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

arXiv:2608.27142v1 Announce Type: new Abstract: Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formu

Applications

Graph-Based Modeling of Financial Volatility Dynamics

arXiv:2608.26127v1 Announce Type: cross Abstract: Accurate forecasting of realized volatility (RV) is crucial for risk management and derivatives pricing. Although the implied volatility (IV) surface

applicationsarxiv-cs-cl

Tutorials

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

arXiv:2604.02324v2 Announce Type: replace-cross Abstract: Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in

tutorialsarxiv-cs-ai

Interpreting Latent Protein Language Model Features with Geometric Annotations

arXiv:2608.26419v1 Announce Type: cross Abstract: Protein language models (pLMs) encode information about protein sequences which enable downstream tasks such as structure prediction, but their intern

J-Zero: Unified Challenger--Solver--Judge Co-Evolution from Zero Data

arXiv:2608.26582v1 Announce Type: cross Abstract: Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human sup

Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift

arXiv:2608.26889v1 Announce Type: new Abstract: Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online all

safetyarxiv-cs-ai

Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot

arXiv:2606.15810v2 Announce Type: replace-cross Abstract: Large language models deployed as commercial APIs are vulnerable to model extraction attacks, while existing defenses either act too late or d

Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models

arXiv:2608.26150v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have created new opportunities to streamline and potentially automate many research processes, inclu

LiveSim: Simulating Environment-Shaped Users in Multi-Agent Live-Stream Ecosystems

arXiv:2608.26849v1 Announce Type: new Abstract: User behavior simulation with large language models~(LLMs) is increasingly used to support multi-agent ecosystem simulation. Existing simulators typical

Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization

arXiv:2608.26288v1 Announce Type: new Abstract: Muon has emerged as a strong optimizer for the matrix-valued parameters in large language model pretraining, approximately orthogonalizing its momentum

Nomad: Autonomous Exploration and Discovery

arXiv:2603.29353v3 Announce Type: replace Abstract: We introduce Nomad, a system for autonomous data exploration and insight discovery. Given a corpus of documents, databases, or other data sources, u

PILOT in the Loop: Live Self-Improvement for Long-Horizon Agents

arXiv:2608.26530v1 Announce Type: new Abstract: Long-horizon agent runs generate experience that can improve both the current run and future work. Most self-improvement methods process this experience

Preserving General Capabilities during Domain Specialization with Uncertainty-Calibrated MOPD

arXiv:2608.26735v1 Announce Type: new Abstract: Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities such as reasoning, codi

safetyarxiv-cs-cl

Procedura: Agentic 3D Modeling with Procedural Control

model-releasesarxiv-cs-cv

arXiv:2608.26238v1 Announce Type: new Abstract: Native 3D generators now recover impressive mesh geometry from a single image. However, a dense mesh stays soft where a machined object should be sharp,

RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature

arXiv:2608.27394v1 Announce Type: new Abstract: Retrieved scientific literature can serve as inspiration for both human and AI scientists. Inspiration can take different forms: prior work may directly

Reasoning about In-Context Samples for Machine-Translation

arXiv:2608.27036v1 Announce Type: new Abstract: Large Language Models (LLMs) can be trained to perform chain-of-thoughts reasoning in order to improve the reliability of their responses. In this work,

Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning

arXiv:2608.26732v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reli

Local Ai

Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information

arXiv:2608.27417v1 Announce Type: new Abstract: Vision-language models (VLMs) can locate an image region referred to by a text prompt and route the corresponding visual evidence to the output, yet the

local-aiarxiv-cs-cv

SAGE: Variate-Wise Semantic Augmentation for Vision-Language Time Series Forecasting

arXiv:2608.26829v1 Announce Type: cross Abstract: Time series forecasting models operate on raw numerical sequences, lacking the semantic knowledge that domain experts implicitly leverage, such as the

safetyarxiv-cs-cv

SLM-Conditioned Hierarchical Relation Routing for Labeled Property Graph Learning

arXiv:2608.26132v1 Announce Type: cross Abstract: Labeled property graphs combine relational structure with heterogeneous textual and categorical properties attached to both nodes and relationships. C

SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers

arXiv:2608.26836v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reason

The Latent Diagnostic Taxonomy: A Framework for Constructing Classifiers and Diagnosing Their Decisions, Applied to Prompt Injection Detection

arXiv:2608.26423v1 Announce Type: cross Abstract: This paper proposes a framework for constructing a classifier as a safeguard layer, and for developing a complementary diagnostic that identifies whic

safetyarxiv-cs-ai

Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators

arXiv:2608.26148v1 Announce Type: new Abstract: Depression affects millions worldwide, yet diagnosis relies on subjective self-reports that may miss authentic behavior. This paper presents an approach

TwinKV: A Composable Repair Pass for KV Cache Eviction via Pairwise Key Redundancy

arXiv:2608.27128v1 Announce Type: new Abstract: Long-context inference is bottlenecked by the memory footprint of the key-value (KV) cache, especially for small models under tight resource budgets. Ex

Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification

arXiv:2608.27311v1 Announce Type: new Abstract: Agent harnesses shape how language-model agents use instructions, tools, and runtime components, but adapting these harnesses requires costly verificati

AlgoTrace: Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models

arXiv:2510.15987v3 Announce Type: replace Abstract: How do inference time and latent computations enable large language models (LLMs) to solve multi-step reasoning problems? We introduce AlgoTrace, a

AutoVerifier: Residual-Guided Non-Parametric Optimization for Reference-Based Answer Verification

arXiv:2608.25637v1 Announce Type: new Abstract: Reference-based verifiers are important for evaluating reasoning models and providing accurate outcome rewards in reinforcement learning with verifiable

CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition

arXiv:2608.25871v1 Announce Type: new Abstract: Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future

safetyarxiv-cs-lg

DualOPSD: Adaptive Privileged Teachers for On-Policy Self-Distillation

arXiv:2608.26019v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a privileged copy of the student model to provide dense supervision without an external teacher. OPSD keeps this

safetyarxiv-cs-lg

EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation

arXiv:2511.11635v2 Announce Type: replace-cross Abstract: In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirem

Epistemic Memory: A Validity Layer for Self-Maintaining Intelligent Systems

arXiv:2510.16899v2 Announce Type: replace Abstract: AI memory mechanisms primarily focus on preserving information content, often neglecting the validity conditions under which knowledge remains appli

agentsarxiv-cs-lg

GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints

arXiv:2601.16905v3 Announce Type: replace Abstract: Machine unlearning in Mixture-of-Experts (MoE) large language models presents a critical yet under-explored challenge. Current unlearning methods ap

How Robust Are Automated Fact-Checking Systems? A Cross-Benchmark Evaluation

arXiv:2608.25934v1 Announce Type: cross Abstract: Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a

JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution

arXiv:2608.25593v1 Announce Type: new Abstract: Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/s

MathAdv: What Theorem Provers Know, Reason, Formalize, and Generalize

arXiv:2608.25449v1 Announce Type: new Abstract: Formal theorem proving enables machine-verifiable evaluation of mathematical reasoning, yet existing benchmarks often emphasize aggregate proof accuracy

ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability

arXiv:2510.09062v2 Announce Type: replace Abstract: Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects cr

SciMIF: Understanding Multimodal Instruction Following in Scientific Domains

arXiv:2608.25973v1 Announce Type: cross Abstract: Understanding instruction-following capabilities in scientific domains is essential for effectively leveraging Multimodal Large Language Models (MLLMs

Tutorials

Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon

arXiv:2608.25990v1 Announce Type: new Abstract: Orthogonal optimisers such as Muon can substantially accelerate large language model pretraining relative to Adam, yet the mechanism remains incompletel

tutorialsarxiv-cs-lg

StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models

model-releasesarxiv-cs-cv

arXiv:2608.26067v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a sin