Search: “models”

Scaling Categorical Flow Maps

arXiv:2605.07820v1 Announce Type: new Abstract: Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they u

11 May 2026

How BASF manages thousands of supply chain decisions with AlphaEvolve’s agentic algorithms

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The agricultural and crop protection supply chain is one of the most intricate networks in the world. It takes up to two years to turn active ingredients into the final products farmers need, and a si

model-releasesgoogle-cloud-ai

7 May 2026

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

6 May 2026

Prosa: Rubric-Based Evaluation of LLMs on Real User Chats in Brazilian Portuguese

arXiv:2605.01630v1 Announce Type: new Abstract: Rankings produced by holistic LLM-as-a-judge scoring are sensitive to the bias of the chosen judge model. We show that switching to binary rubric scorin

5 May 2026

Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs

arXiv:2605.00674v1 Announce Type: new Abstract: Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating

4 May 2026

Token Arena: A Continuous Benchmark Unifying Energy and Cognition in AI Inference

arXiv:2605.00300v1 Announce Type: cross Abstract: Public inference benchmarks compare AI systems at the model and provider level, but the unit at which deployment decisions are actually made is the en

4 May 2026

Low Rank Adaptation for Adversarial Perturbation

arXiv:2604.27487v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved the t

1 May 2026

The Impact of LLM Self-Consistency and Reasoning Effort on Automated Scoring Accuracy and Cost

arXiv:2604.26954v1 Announce Type: cross Abstract: Strategic model selection and reasoning settings are more effective than ensembling for optimizing automated scoring with large language models (LLMs)

1 May 2026

Entropy Centroids as Intrinsic Rewards for Test-Time Scaling

arXiv:2604.26173v1 Announce Type: cross Abstract: An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy

MATH-PT: A Math Reasoning Benchmark for European and Brazilian Portuguese

arXiv:2604.25926v1 Announce Type: new Abstract: The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and b

Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens

arXiv:2604.26355v1 Announce Type: new Abstract: Reasoning in Large Language Models incurs significant inference-time compute, yet the token-level information structure of reasoning traces remains unde

TildeOpen LLM: Leveraging Curriculum Learning to Achieve Equitable Language Representation

arXiv:2603.08182v2 Announce Type: replace-cross Abstract: Large language models often underperform in many European languages due to the dominance of English and a few high-resource languages in train

TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation

arXiv:2503.04872v3 Announce Type: replace-cross Abstract: The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. Howev

Benchmarking and Adapting On-Device LLMs for Clinical Decision Support

arXiv:2601.03266v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly advanced in clinical decision-making, yet the deployment of proprietary systems is hindered by privacy con

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Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers

arXiv:2604.25891v1 Announce Type: new Abstract: Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavio

safetyarxiv-cs-lg

CRAFT: Grounded Multi-Agent Coordination Under Partial Information

arXiv:2603.25268v2 Announce Type: replace Abstract: We introduce CRAFT, a multi-agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In thi

Local Ai

Dual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs

arXiv:2604.25039v1 Announce Type: new Abstract: Large Language Models (LLMs) solve many reasoning tasks via chain-of-thought (CoT) prompting, but smaller models (about 7 to 8B parameters) still strugg

local-aiarxiv-cs-cl

Feasible-First Exploration for Constrained ML Deployment Optimization in Crash-Prone Hierarchical Search Spaces

arXiv:2604.25073v1 Announce Type: new Abstract: Deploying machine learning models under production constraints requires joint optimization over model family, quantization scheme, runtime backend, and

Scaling Probabilistic Transformer via Efficient Cross-Scale Hyperparameter Transfer

arXiv:2604.25409v1 Announce Type: new Abstract: Probabilistic Transformer (PT), a white-box probabilistic model for contextual word representation, has demonstrated substantial similarity to standard

Tutorials

ViPO: Visual Preference Optimization at Scale

arXiv:2604.24953v1 Announce Type: new Abstract: While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Curr

tutorialsarxiv-cs-cv

Domain Fine-Tuning vs. Retrieval-Augmented Generation for Medical Multiple-Choice Question Answering: A Controlled Comparison at the 4B-Parameter Scale

arXiv:2604.23801v1 Announce Type: new Abstract: Practitioners deploying small open-weight large language models (LLMs) for medical question answering face a recurring design choice: invest in a domain

Tutorials

Generating Verifiable Chain of Thoughts from Exection-Traces

arXiv:2512.00127v3 Announce Type: replace-cross Abstract: Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic C

tutorialsarxiv-cs-ai

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Peer Identity Bias in Multi-Agent LLM Evaluation: An Empirical Study Using the TRUST Democratic Discourse Analysis Pipeline

arXiv:2604.22971v1 Announce Type: cross Abstract: The TRUST democratic discourse analysis pipeline exposes its large language model (LLM) components to peer model identity through multiple structural

safetyarxiv-cs-ai

Scaling Multi-Node Mixture-of-Experts Inference Using Expert Activation Patterns

arXiv:2604.23150v1 Announce Type: cross Abstract: Most recent state-of-the-art (SOTA) large language models (LLMs) use Mixture-of-Experts (MoE) architectures to scale model capacity without proportion

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation

arXiv:2505.16637v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities in machine translation (MT). However, most advanced MT-specifi

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation

arXiv:2604.24763v1 Announce Type: new Abstract: Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creatin

researcharxiv-cs-cv

From Words to Amino Acids: Does the Curse of Depth Persist?

arXiv:2602.21750v2 Announce Type: replace Abstract: Protein language models (PLMs) have become widely adopted as general-purpose models, demonstrating strong performance in protein engineering and de

27 Apr 2026

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining

arXiv:2511.18903v2 Announce Type: replace-cross Abstract: Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even

27 Apr 2026

Toward Automated Robustness Evaluation of Mathematical Reasoning

arXiv:2506.05038v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks. However, these models exhibit unexpecte

27 Apr 2026

Context Is What You Need: The Maximum Effective Context Window for Real World Limits of LLMs

arXiv:2509.21361v2 Announce Type: replace-cross Abstract: Large language model (LLM) providers boast big numbers for maximum context window sizes. To test the real world use of context windows, we 1)

24 Apr 2026

DeepSeek-V4: a million-token context that agents can actually use

model-releaseshugging-face

DeepSeek-V4 is an advanced language model featuring a million-token context window that enables practical agentic applications beyond simple retrieval. The model demonstrates improved efficiency and u

24 Apr 2026

Measuring Opinion Bias and Sycophancy via LLM-based Coercion

arXiv:2604.21564v1 Announce Type: new Abstract: Large language models increasingly shape the information people consume: they are embedded in search, consulted for professional advice, deployed as age

24 Apr 2026

Bootstrapping Post-training Signals for Open-ended Tasks via Rubric-based Self-play on Pre-training Text

arXiv:2604.20051v1 Announce Type: new Abstract: Self-play has recently emerged as a promising paradigm to train Large Language Models (LLMs). In self-play, the target LLM creates the task input (e.g.,

CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values

arXiv:2509.03740v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) like CLIP have shown impressive zero-shot and few-shot learning capabilities across diverse applications. Howeve

HiPO: Hierarchical Preference Optimization for Adaptive Reasoning in LLMs

arXiv:2604.20140v1 Announce Type: new Abstract: Direct Preference Optimization (DPO) is an effective framework for aligning large language models with human preferences, but it struggles with complex

LLAMADRS: Evaluating Open-Source LLMs on Real Clinical Interviews--To Reason or Not to Reason?

arXiv:2501.03624v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel on many NLP benchmarks, but their behavior on real-world, semi-structured prediction remains underexplored.

New innovations in Google Distributed Cloud

model-releasesgoogle-cloud-ai

Today at Google Cloud Next, we’re announcing new capabilities in Google Distributed Cloud (GDC) that bring Gemini and our advanced AI stack to wherever your data is, so you don’t need to compromise be

ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation

arXiv:2604.19144v1 Announce Type: new Abstract: Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly ad

Right for the Wrong Reasons: Epistemic Regret Minimization for LLM Causal Reasoning

arXiv:2602.11675v3 Announce Type: replace Abstract: Large language models may answer causal questions correctly for the wrong reasons, substituting associational shortcuts P(Y|X) for the interventiona

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images

arXiv:2509.07966v2 Announce Type: replace-cross Abstract: Visual reasoning over structured data such as tables is a critical capability for modern vision-language models (VLMs), yet current benchmarks

📢 Kimi K2.6 API is live • Input Price (Cache Hit): 0.16 / M tokens • Input Price (Cache Miss): 0.95 / M tokens • Output: $4.00 / M tokens…

model-releaseskimi-moonshot--x

📢 Kimi K2.6 API is live • Input Price (Cache Hit): 0.16 / M tokens • Input Price (Cache Miss): 0.95 / M tokens • Output: $4.00 / M tokens Kimi K2.6 is our latest + most intelligent model - stronger lo

Logit Arithmetic Elicits Long Reasoning Capabilities Without Training

arXiv:2510.09354v2 Announce Type: replace Abstract: Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabi

Predicting LLM Compression Degradation from Spectral Statistics

arXiv:2604.18085v1 Announce Type: new Abstract: Matrix-level low-rank compression is a promising way to reduce the cost of large language models, but running compression and evaluating the resulting m

PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces

arXiv:2510.18109v4 Announce Type: replace-cross Abstract: Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model bu

Local Ai

Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement

arXiv:2604.16858v1 Announce Type: new Abstract: Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demand

local-aiarxiv-cs-cv

RefineStat: Efficient Exploration for Probabilistic Program Synthesis

arXiv:2509.01082v3 Announce Type: replace Abstract: Probabilistic programming offers a powerful framework for modeling uncertainty, yet statistical model discovery in this domain entails navigating an

Toward Efficient Influence Function: Dropout as a Compression Tool

arXiv:2509.15651v2 Announce Type: replace Abstract: Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency

Local Ai

VideoThinker: Building Agentic VideoLLMs with LLM-Guided Tool Reasoning

arXiv:2601.15724v2 Announce Type: replace Abstract: Long-form video understanding remains a fundamental challenge for current Video Large Language Models. Most existing models rely on static reasoning

local-aiarxiv-cs-cv