Search: “model-releases”

Riemannian-Manifold Steering: Geometry-Aware Generative Autoencoders for Label-Free Steering

arXiv:2605.24942v1 Announce Type: cross Abstract: Steering a language model - intervening on its internal activations to change downstream behaviour - has recently expanded beyond linear interpolation

Model Releases

RL with Learnable Textual Feedback: A Bilevel Approach

DGX agent

arXiv:2605.24547v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards can improve LLM reasoning, but learning remains sample-inefficient when terminal rewards are sparse. This

model-releasesarxiv-cs-lg

26 May 2026

RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments

model-releasesarxiv-cs-ro

arXiv:2509.17057v3 Announce Type: replace Abstract: We present RoboManipBaselines, an open-source software framework for imitation learning research in robotic manipulation. The framework supports the

Robust Fuzzy Multi-view Learning under View Conflict

arXiv:2605.24475v1 Announce Type: cross Abstract: Trusted multi-view classification aims to deliver reliable fusion for accurate predictions and has recently attracted substantial attention in both ac

RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism

arXiv:2605.25565v1 Announce Type: cross Abstract: While Large Language Models (LLMs) are commonly fine-tuned to handle domain-specific tasks before being applied to vertical applications, adapting the

SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation

arXiv:2605.25984v1 Announce Type: cross Abstract: Ensuring safe and contextually appropriate behaviour in Large Language Models (LLMs) remains a critical challenge for real-world deployment. We presen

Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed

arXiv:2601.21094v2 Announce Type: replace-cross Abstract: Safe Reinforcement Learning (RL) algorithms are typically evaluated under fixed training conditions. We investigate whether training-time safe

SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks

arXiv:2605.25492v1 Announce Type: new Abstract: Pairwise model comparisons drawn from foundation-model benchmarks ('A is safer than B') are read as quantitative verdicts but hinge on harness choices b

ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training

arXiv:2605.24326v1 Announce Type: cross Abstract: The rapid scaling of large language model training requires distributing GPU resources across multiple data center buildings and regions. We refer to

Schema-Grounded LLM Extraction for FHIR Patient Digital Twins

arXiv:2601.05847v2 Announce Type: replace Abstract: We revisit the problem of constructing interoperable patient digital twins from unstructured electronic health records (EHRs) and argue that the tas

Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models

arXiv:2605.25394v1 Announce Type: new Abstract: Large language models often generate confident but incorrect answers rather than abstaining when uncertain. This problem is particularly acute for small

Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions

arXiv:2605.25073v1 Announce Type: cross Abstract: Background: Fine-tuning is central to adapting pre-trained Large Language Models (LLMs) to downstream tasks, but its reliance on training data, parame

SemanticZip: A Pilot Framework for Lossy Text Compression with LLMs as Semantic Decompressors

arXiv:2605.24541v1 Announce Type: cross Abstract: Text compression for large language model (LLM) systems is usually framed as token deletion, retrieval, summarization, or exact reconstruction. We stu

'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning

arXiv:2605.25548v1 Announce Type: cross Abstract: Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. Temporal-first approaches build pe

SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking

arXiv:2605.25160v1 Announce Type: new Abstract: Mobile GUI agents powered by large language models have progressed rapidly, creating urgent needs for realistic and comprehensive evaluation. Existing b

SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills

arXiv:2605.24117v1 Announce Type: new Abstract: Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience c

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning

arXiv:2605.23969v1 Announce Type: new Abstract: Instruction tuning has optimized the specialized capabilities of large language models (LLMs), but it often requires extensive datasets and prolonged tr

Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers

arXiv:2605.25949v1 Announce Type: cross Abstract: Neural PDE solvers have followed the scaling trajectory of vision and language, with recent foundation models reaching billions of parameters. We argu

Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction

arXiv:2512.05402v2 Announce Type: replace-cross Abstract: Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven r

SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks?

arXiv:2605.21740v2 Announce Type: replace Abstract: LLM agents have incredible potential for scientific discovery applications. However, the performance of LLM agents on real-world, small molecule dru

SODE: Analyzing Social Dynamics in LLM Agents

arXiv:2605.23949v1 Announce Type: cross Abstract: As Large Language Models (LLMs) evolve into interactive agents, understanding their behavioral alignment within human social dynamics becomes essentia

SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models

arXiv:2506.18543v2 Announce Type: replace-cross Abstract: The rapid proliferation of Large Language Models (LLMs) has heightened concerns regarding their exposure to jailbreak attacks, which craft adv

SomaliBench Eval: Measuring English-to-Somali Refusal Gaps in Open-Weight Language Models

arXiv:2605.25420v1 Announce Type: cross Abstract: Large language model safety evaluation remains heavily English-centered, leaving low-resource languages under-measured even when models are deployed g

Some ideas for what comes next, May 2026

model-releasesinterconnects

This article from Interconnects explores speculative ideas and predictions about technological developments and trends expected around May 2026, likely covering emerging AI capabilities, infrastructur

Sources: Chinese government agencies begin imposing overseas travel restrictions on individuals involved in advanced AI work, including at Alibaba and DeepSeek (Bloomberg)

Bloomberg: Sources: Chinese government agencies begin imposing overseas travel restrictions on individuals involved in advanced AI work, including at Alibaba and DeepSeek — China is restricting overse

model-releasestechmeme

Spectral Probe-Circuits: A Three-Step Recipe for Identifying Attention-Head Circuits in Pretrained Transformers

arXiv:2605.24059v1 Announce Type: cross Abstract: We present a three-step recipe for identifying attention-head circuits in pretrained transformers. A per-head spectral signal -- the time-integrated p

Spectral Retrieval: Multi-Scale Sinc Convolution over Token Embeddings for Localized Retrieval in LLM Multi-Agent Systems

arXiv:2605.24764v1 Announce Type: cross Abstract: [Abridged] - Spectral Retrieval is a plug-in re-ranking stage that interpolates between per-token MaxSim and mean-pool retrieval through a multi-scale

Split-Merge: A Difference-based Approach for Dominant Eigenvalue Problem

arXiv:2501.15131v3 Announce Type: replace-cross Abstract: The computation of the dominant eigenpair for symmetric positive semidefinite matrices is fundamental in numerical optimization. This work shi

Spotify launches a library of over 650 narrated long-form magazine articles in English for Premium users; free users can buy articles 'individually for $1.99' (Jess Weatherbed/The Verge)

Jess Weatherbed / The Verge: Spotify launches a library of over 650 narrated long-form magazine articles in English for Premium users; free users can buy articles “individually for $1.99” — More than

model-releasestechmeme

Stein Variational Ergodic Surface Coverage with SE(3) Constraints

model-releasesarxiv-cs-ro

arXiv:2603.09458v3 Announce Type: replace Abstract: Surface manipulation tasks require robots to generate trajectories that comprehensively cover complex 3D surfaces while maintaining precise end-effe

Stochastic Estimation of the Layer-wise Hessian Trace for Monitoring Neural-network Training

arXiv:2605.25674v1 Announce Type: new Abstract: The loss and the norm of its gradient separate the healthy and the pathological regimes of neural-network training only weakly, whilst the curvature of

Stochastic Linear Bandits with Parameter Noise

arXiv:2601.23164v2 Announce Type: replace Abstract: We study the stochastic linear bandits with parameter noise model, in which the reward of action a is a^op heta where heta is sampled i.i.d. We show

STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media

arXiv:2605.25162v1 Announce Type: cross Abstract: Large language models for vertical domains are bottlenecked by the scarcity of complex, domain-specific task-oriented dialogues. Existing data acquisi

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning

arXiv:2605.24709v1 Announce Type: new Abstract: Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process da

StreamProfileBench: A Benchmark for Fine-Grained User Profile Inference in Real-World Streaming Scenarios

arXiv:2605.25758v1 Announce Type: new Abstract: Large Language Models (LLMs) have reshaped user profiling, yet current evaluations mainly focus on static data snapshots. This paradigm overlooks the re

StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs

arXiv:2605.25534v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at structural reasoning yet suffer from a sharp logical brittleness in structural consistency. We term th

Structural Abstraction as an Inductive Bias for Non-Stationary Language Model Training

arXiv:2603.17198v2 Announce Type: replace-cross Abstract: A foundational principle in cognitive science holds that intelligent agents do not learn by storing experiences as isolated instances, but by

SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors

arXiv:2502.11167v5 Announce Type: replace-cross Abstract: Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineer…

System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineering leverage actually sits. The labs own the model. You own

model-releasesdair-ai--x

Teaching large language models to reason like expert diagnosticians

arXiv:2509.12194v2 Announce Type: replace Abstract: Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the

Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation

arXiv:2605.24211v1 Announce Type: cross Abstract: Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) conti

Test-Time Graph Search for Goal-Conditioned Reinforcement Learning

arXiv:2510.07257v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) often struggles with long-horizon tasks, where errors in value estimation accumulate and prod

Test-Time Self-Adaptive Conditioning for Stable Audio-Driven Talking-Head Generation

arXiv:2605.25488v1 Announce Type: cross Abstract: Audio-driven talking-head generation has achieved remarkable progress with recent models such as AniTalker, FLOAT, and Sonic. Despite their success, m

The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models

arXiv:2605.25510v1 Announce Type: new Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require

the basic trick to using Claude Code for non-technical work is to put a bunch of files in a folder and tell it can write scripts + make HTML

Claude Code can be used for non-technical work by organizing files in a folder and instructing it to write scripts and create HTML documents. This approach allows users without programming expertise t

model-releasesthariq--x

The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks

arXiv:2404.00176v3 Announce Type: replace Abstract: Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-le

The Model Is Not the Product: A Dual-Pillar Architecture for Local-First Psychological Coaching

arXiv:2605.24411v1 Announce Type: new Abstract: Existing language model applications struggle to meet the demand for emotionally oriented support, primarily due to their inability to maintain deep, pe

The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench

arXiv:2605.24782v1 Announce Type: new Abstract: While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than u