Search: “model-releases”

Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls

arXiv:2605.03147v1 Announce Type: new Abstract: Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult. Unlike t

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Efficient Deconvolution in Populational Inverse Problems

DGX agent

arXiv:2505.19841v2 Announce Type: replace-cross Abstract: This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations

model-releasesarxiv-cs-lg

6 May 2026

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity

arXiv:2605.03667v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving

arXiv:2509.17677v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance on mathematical reasoning under well-defined conditions. However, real-world engineering

EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents

arXiv:2605.01250v1 Announce Type: new Abstract: Earth Observation (EO) analysis is inherently interactive: resolving uncertainty often requires expanding the region of interest, retrieving historical

eOptShrinkQ: Near-Lossless KV Cache Compression Through Optimal Spectral Denoising and Quantization

arXiv:2605.02905v1 Announce Type: new Abstract: We show that the key-value (KV) cache in transformer attention heads admits a natural decomposition into a low-rank shared context component and a full-

EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage

arXiv:2605.03998v1 Announce Type: new Abstract: Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender dispa

Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

arXiv:2605.03547v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content such as characters and logo

Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for controlled, single-s

Expanding functional protein sequence space using high entropy generative models

arXiv:2605.03578v1 Announce Type: cross Abstract: Boltzmann Machines trained on evolutionary sequence data have emerged as a powerful paradigm for the data-driven design of artificial proteins. Howeve

Exposing LLM Safety Gaps Through Mathematical Encoding:New Attacks and Systematic Analysis

arXiv:2605.03441v1 Announce Type: cross Abstract: Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We s

FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection

arXiv:2605.03294v1 Announce Type: new Abstract: Open-vocabulary object detection often fails under distribution shifts, as it can be misled by spurious correlations between non-causal visual attribute

Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators

arXiv:2605.03969v1 Announce Type: new Abstract: AI-generated text is nowadays produced at scale across domains and heterogeneous generation pipelines, making robustness to distribution shift a central

FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction

arXiv:2605.01726v1 Announce Type: cross Abstract: Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-dom

FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

arXiv:2605.03460v1 Announce Type: cross Abstract: Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which

FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution

arXiv:2605.03749v1 Announce Type: new Abstract: Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by

From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT

arXiv:2605.03686v1 Announce Type: cross Abstract: Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and ne

From Laboratory to Real-World Applications: Benchmarking Agentic Code Reasoning at the Repository Level

arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consiste

From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework

arXiv:2605.02939v1 Announce Type: new Abstract: Multimodal controversy detection (MCD) identifies controversial content in videos and their associated user comments, to support risk management for soc

Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability

arXiv:2605.03196v1 Announce Type: new Abstract: A reliable language model should be able to signal, prior to generation, when a query falls outside its knowledge. We investigate whether representation

GOAT: A Training Framework for Goal-Oriented Agent with Tools

arXiv:2510.12218v2 Announce Type: replace Abstract: Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning

Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories

arXiv:2605.03901v1 Announce Type: cross Abstract: Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective

Graph Reconstruction from Differentially Private GNN Explanations

arXiv:2605.03388v1 Announce Type: new Abstract: Regulatory frameworks such as GDPR increasingly require that ML predictions be accompanied by post-hoc explanations, even when raw data and trained mode

HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems

arXiv:2605.02832v1 Announce Type: new Abstract: Deciding how to distribute work between humans and AI systems is a central challenge in organisational design. Most approaches treat this as a binary ch

Hierarchical Memorization in Large Language Models: Evidence from Citation Generation

arXiv:2511.08877v2 Announce Type: replace Abstract: Large language models (LLMs) generate fluent text across a wide range of tasks, but the fabrication of non-existent academic citations remains a cri

HistCAD: A Constraint-Aware Parametric History-Based CAD Representation, Dataset, and Benchmark with Industrial Complexity

arXiv:2602.19171v2 Announce Type: replace-cross Abstract: Parametric CAD sequences are reusable because dimensional and geometric constraints govern how parameter changes propagate. Existing CAD gener

How Language Models Process Negation

arXiv:2605.03052v1 Announce Type: new Abstract: We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong

Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic

arXiv:2510.09472v2 Announce Type: replace Abstract: Despite the remarkable progress in neural models, their ability to generalize, a cornerstone for applications such as logical reasoning, remains a c

'I Don't Know' -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation

arXiv:2605.00957v1 Announce Type: cross Abstract: Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LL

IRIS: Intent Resolution via Inference-time Saccades for Open-Ended VQA in Large Vision-Language Models

arXiv:2602.16138v2 Announce Type: replace Abstract: We introduce IRIS (Intent Resolution via Inference-time Saccades), a novel training-free approach that uses eye-tracking data in real-time to resolv

ISAAC: Auditing Causal Reasoning in Deep Models for Drug-Target Interaction

arXiv:2605.02962v1 Announce Type: new Abstract: Deep learning models for drug--target interaction (DTI) prediction often achieve strong benchmark performance without necessarily relying on mechanistic

Jiao: Bridging Isolation and Customization in Mixed Criticality Robotics

model-releasesarxiv-cs-ro

arXiv:2605.03641v1 Announce Type: new Abstract: Consumer robotics demands consolidation of safety-critical control, perception pipelines, and user applications on shared multicore platforms. While sta

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

arXiv:2605.02965v1 Announce Type: new Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content,

Kernel Affine Hull Machines for Compute-Efficient Query-Side Semantic Encoding

arXiv:2605.02950v1 Announce Type: new Abstract: Transformer-based semantic retrieval is highly effective, yet in many deployments the dominant cost lies in online query encoding rather than corpus ind

Label-Efficient School Detection from Aerial Imagery via Weakly Supervised Pretraining and Fine-Tuning

arXiv:2605.03968v1 Announce Type: new Abstract: Accurate school detection is essential for supporting education initiatives, including infrastructure planning and expanding internet connectivity to un

Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective

arXiv:2605.03373v1 Announce Type: new Abstract: Classical optimization theory establishes that zeroth-order (ZO) algorithms suffer from a dimension-dependent slowdown, with convergence rates typically

LightSBB-M: Bridging Schrodinger and Bass for Generative Diffusion Modeling

arXiv:2601.19312v2 Announce Type: replace Abstract: The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodi

LitVISTA: A Benchmark for Narrative Orchestration in Literary Text

arXiv:2601.06445v2 Announce Type: replace Abstract: Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can gener

LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation

arXiv:2605.01394v1 Announce Type: cross Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Alth

LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

arXiv:2605.03328v1 Announce Type: new Abstract: Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse

Low Rank Tensor Completion via Adaptive ADMM

arXiv:2605.03736v1 Announce Type: cross Abstract: We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-r

Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models

arXiv:2605.03438v1 Announce Type: new Abstract: Pre-trained 3D point cloud foundation models (PFMs) have demonstrated strong transferability across diverse downstream tasks. However, full fine-tuning

MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation

arXiv:2605.01486v1 Announce Type: new Abstract: Legal consultation is a high-stakes, knowledge-intensive task that requires agents to identify relevant legal issues, retrieve authoritative support, an

Maximizing mutual information between prompts and responses improve LLM personalization with no additional data or human oversight

arXiv:2603.19294v2 Announce Type: replace-cross Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labe

MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following

arXiv:2605.03858v1 Announce Type: new Abstract: Multi-constraint instruction following requires verifying whether a response satisfies multiple individual requirements, yet LLM judges are often assess

MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers

arXiv:2602.00933v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is rapidly becoming the standard interface for Large Language Models (LLMs) to discover and invoke external t

MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports

arXiv:2605.03103v1 Announce Type: new Abstract: Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical h

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

arXiv:2605.03511v1 Announce Type: new Abstract: Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in s