Search: “models”

arXiv:2605.27819v1 Announce Type: cross Abstract: Sparse autoencoders are usually trained one layer at a time, even though transformer residual stream activations are strongly coupled across depth. Th

arXiv:2605.27765v1 Announce Type: cross Abstract: Self-Distillation Policy Optimization (SDPO) provides dense token-level credit assignment for reinforcement learning with large language models by lev

arXiv:2605.27996v1 Announce Type: new Abstract: Single-axis mitigations of reward-model biases (e.g., reducing proxy reliance on length, sycophancy, or style) can rotate optimization pressure onto cor

arXiv:2605.27959v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have increasingly localized and interleaved visual evidence for deliberative reasoning. Grounding-based appro

arXiv:2605.28735v1 Announce Type: new Abstract: Transparent objects are common in daily life, and it is important to understand their multilayer depth, including the transparent surface and the object

arXiv:2605.28442v1 Announce Type: cross Abstract: Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation

arXiv:2605.27845v1 Announce Type: cross Abstract: Financial and economic research often relies on structured supply-chain disclosures and commercial databases. In China, supplier--customer disclosure

arXiv:2605.27756v1 Announce Type: cross Abstract: High-performance computing enables simulation of high-dimensional physical systems, but downstream analyses such as inverse problems and control remai

arXiv:2603.07860v2 Announce Type: replace Abstract: Pretrained diffusion models are effective priors for Bayesian inverse problems, but posterior sampling with these priors is often costly because dat

arXiv:2605.28517v1 Announce Type: cross Abstract: Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimization proper

arXiv:2605.27927v1 Announce Type: new Abstract: Image inputs enable Large Vision Language Models (LVLMs) to perceive fine-grained visual information, but also introduce a pixel-level attack surface th

arXiv:2605.28594v1 Announce Type: cross Abstract: In this article, we present an improved version of the PULSE method (Partition function Unsupervised Learning Sampling and Evaluation) for estimating

this is so funny, training opus 4.7 on business skills makes it misaligned and dishonest 😭 Learnings from testing Claude Opus 4.8: > Much worse than Opus 4.7 and GPT 5.5 on Vending Bench > More aligne

arXiv:2605.27401v1 Announce Type: cross Abstract: There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous g

arXiv:2605.28023v1 Announce Type: cross Abstract: Visual captioning requires models to capture visual content faithfully while minimizing both omission and hallucination. As the dominant paradigm for

arXiv:2605.28065v1 Announce Type: new Abstract: AI agents are increasingly useful for biological data analysis, but existing benchmarks mostly test broad biological knowledge, executable workflows, or

arXiv:2605.28229v1 Announce Type: cross Abstract: With the rapid development of pre-training technologies, adapting large-scale Vision-Language Models (VLMs) for video understanding ie image-to-video

arXiv:2605.28422v1 Announce Type: cross Abstract: Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead

DeepMind's AlphaFold3 and related tools have generated a comprehensive atlas containing over one billion predicted protein structures and additional billions of protein sequences, representing a major

arXiv:2605.26203v1 Announce Type: cross Abstract: The success of deployed agents relies on their ability to handle open-ended user requests using their inherent capabilities, not only in solving reque

arXiv:2605.26419v1 Announce Type: new Abstract: Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unse

arXiv:2511.02525v2 Announce Type: replace-cross Abstract: The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making lo

arXiv:2605.26321v1 Announce Type: new Abstract: AI agents are beginning to complete valuable, long-horizon business operations tasks, but training and evaluation environments for enterprise work still

arXiv:2603.13853v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) connects large language models (LLMs) to external knowledge, but single-round retrieval is often insuffic

Behind the MiMo API Price Reduction: The deepest price cut, up to 99%, is for Input (Cache Hit). The core reason is our inference framework now supports hierarchical KV cache optimization for SWA. Pro

arXiv:2601.08146v3 Announce Type: replace-cross Abstract: Existing circuit discovery methods rely on templated tasks with clean counterfactuals, limiting their use on diverse natural text. We adapt Co

arXiv:2603.03194v2 Announce Type: replace Abstract: Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software en

arXiv:2605.27050v1 Announce Type: new Abstract: We present BhashaSetu, a linguistically enriched English--Marathi parallel dataset addressing persistent data limitations in low-resource neural machine

This article describes how OpenAI's Codex model can be used to build autonomous tax agents capable of self-improvement through code generation and execution. The work demonstrates using large language

arXiv:2605.27000v1 Announce Type: cross Abstract: Repeated sampling with a verifier is the standard way to allocate test-time compute for code generation, with pass@K as the canonical metric. Yet the

arXiv:2605.26537v1 Announce Type: new Abstract: Language Models (LMs) emit Chains-of-Thought (CoTs) that drive much of their capability. However, the same sequence that carries useful reasoning can al

arXiv:2605.27115v1 Announce Type: new Abstract: Domain specialization can improve LLM behavior in vertical domains, but often weakens the general capabilities inherited from the original model. Recent

arXiv:2605.26315v1 Announce Type: cross Abstract: Direct Preference Optimisation (DPO) is widely used for safety alignment in large language models. However, prior work shows it is brittle and exhibit

Michael Nuñez / VentureBeat: Datacurve releases the DeepSWE coding benchmark, a 113-task test across 91 open-source repositories and five languages, and says GPT-5.5 is the leader at 70% — For months,

arXiv:2602.08586v3 Announce Type: replace Abstract: Multi-agent LLM systems consistently outperform single-agent baselines, yet practitioners still cannot predict which design works for a new task or

arXiv:2605.26290v1 Announce Type: new Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media an

arXiv:2605.26680v1 Announce Type: cross Abstract: Recent video multimodal large language models (MLLMs) increasingly couple step-by-step reasoning with on-demand visual evidence retrieval, allowing mo

arXiv:2601.21789v2 Announce Type: replace-cross Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the o

arXiv:2506.09398v3 Announce Type: replace Abstract: We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, c

arXiv:2605.24456v2 Announce Type: replace Abstract: Humans constantly reason about 3D proximity, the relations between their body and surrounding objects, to guide perception and action in daily life.

arXiv:2605.26492v1 Announce Type: cross Abstract: LLM-generated stories are a popular use case, but they show very low variability. We sample 20,000 total stories from four current models using five p

arXiv:2605.26166v1 Announce Type: cross Abstract: The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for adaptive, resource-efficient Intrusion Detection Systems

arXiv:2603.13373v3 Announce Type: replace-cross Abstract: In ubiquitous and mobile health systems, computational models infer human states from wearable, behavioral, and physiological sensing data. In

arXiv:2605.27016v1 Announce Type: cross Abstract: Large language models (LLMs) are prone to hallucinations, i.e., statements unsupported by the input or training data, hindering reliable deployment. I

arXiv:2605.26408v1 Announce Type: new Abstract: Causal discovery in time series is increasingly performed using nonlinear machine-learning models, yet the resulting causal relationships are almost alw

arXiv:2605.26121v1 Announce Type: cross Abstract: LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, optimal mixing is hindered by categorization flaws: