Search: “models”

arXiv:2604.08366v1 Announce Type: cross Abstract: Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, th

arXiv:2604.07054v2 Announce Type: replace Abstract: Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language

arXiv:2512.23365v3 Announce Type: replace Abstract: The rapid progress of Multimodal Large Language Models (MLLMs) has unlocked the potential for enhanced 3D scene understanding and spatial reasoning.

arXiv:2604.07028v1 Announce Type: cross Abstract: Strategic interaction in adversarial domains such as law, diplomacy, and negotiation is mediated by language, yet most game-theoretic models abstract

arXiv:2604.07415v1 Announce Type: cross Abstract: Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often

arXiv:2604.07753v1 Announce Type: cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient c

arXiv:2604.08072v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) excel at extracting local features hierarchically, but their performance in capturing complex correlations hinges h

arXiv:2604.06192v1 Announce Type: cross Abstract: Recent work uses entropy-based signals at multiple representation levels to study reasoning in large language models, but the field remains largely em

arXiv:2604.06501v1 Announce Type: new Abstract: Analogical reasoning is a hallmark of human intelligence, enabling us to solve new problems by transferring knowledge from one situation to another. Yet

arXiv:2604.07894v1 Announce Type: new Abstract: Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preference

arXiv:2510.23642v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, e

Nous Research's Hermes model line has been deliberately developed with a strong emphasis on creative capability. The training dataset for Hermes 4 expanded to 50x more data tokens than Hermes 3, w...

I looked at their prompts, It's complete bs They are literally providing all of the insight to the LLM upfront > Are there any security vulnerabilities in this code? Consider the behavior of the SEQ_L

LangChain launched **Deep Agents Deploy** in beta as an open-source, model-agnostic alternative to Anthropic's Claude Managed Agents, allowing developers to deploy production-ready agents via a sin...

**Paramax** (also referred to as 'Parax' in the Reddit post title) is a small Python library by Daniel Ward that provides parameterizations and parameter constraints for JAX PyTrees, designed to wo...

<channel|>This release update for `local-ai` (version v0.20.5-rc1) expands model compatibility by integrating several popular large language models. It allows users to run models such as Kimi-K2.5,...

'But here is what we found when we tested: We took the specific vulnerabilities Anthropic showcases in their announcement, isolated the relevant code, and ran them through small, cheap, open-weights m

The specific tweet (status ID 2041723225827062080) could not be directly retrieved, but based on closely related content from Ethan Mollick's (@emollick) X account, the most relevant match is the p...

If you're an AI/agent builder, it's so important that you don't overbuild and overcommit on a specific toolset and infrastructure. Frontier labs are shipping not just the models, but the harnesses and

INCREDIBLE GLM-5.1 weights are now opensource > i’ve had early access to the weights for the past few days > and yeah… this one matters a lot benchmarks? > SWE-Bench Pro: 58.4 > beats Opus 4.6 (57.3)

SWE-1.6 is free for everyone in Windsurf for the next 3 months at 200 tok/s. For paying users, we've partnered with Cerebras to serve the model at 950 tok/s. More technical details about this training

Total Parameters 4B Architecture Looped (latent-recurrent) normalized-GPT with interleaved sliding-window attention (SWA) and full-attention layers Precision BF16 Context Length Up to 256K tokens Supp

Defiance puts DavidAU's Turbo-Brilliance 'AI Construct' modes on HauhauCS's uncensored Qwen3.8-27B. You pick a mode (Joker, Dr. House, a 20-agent Einstein panel, a Socratic tutor) per request, and the

arXiv:2606.21550v2 Announce Type: replace Abstract: Alignment from human feedback uses human judgments about model outputs to steer the behavior of language models after pretraining. When those judgme

arXiv:2610.21218v1 Announce Type: new Abstract: Agentic large language models have been documented to lose reasoning capability when placed inside a harness, the software that mediates their observati

arXiv:2610.22137v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world applications often require them to contin

arXiv:2601.01982v2 Announce Type: replace Abstract: Large language models (LLMs) excel at natural language tasks but remain brittle in domains requiring precise logical and symbolic reasoning. Chaotic

A couple of weeks ago I read this Wccftech article about Patrick Moorhead's 4 Spark setup. I meant to post this a few days ago but ran some more tests first, and then my workstation had a problem afte

arXiv:2610.21182v1 Announce Type: new Abstract: Latent chain-of-thought replaces written reasoning steps with continuous hidden states, giving models additional steps to compute before answering. It r

arXiv:2610.22312v1 Announce Type: new Abstract: Spatial instructions describe how to move objects, transform regions, or modify their surroundings. When several operations are applied in sequence, eac

arXiv:2610.22360v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) and 3D generation have expanded 3D understanding and synthesis. However, beyond visual fidel

arXiv:2610.21588v1 Announce Type: new Abstract: Audio-visual large language models (AV-LLMs) have made strong progress in multimodal understanding, yet most treat audio as monaural semantic content, l

arXiv:2610.21520v1 Announce Type: cross Abstract: Joint Acoustic Scene Classification (ASC) and Audio Tagging (AT) models traditionally rely on unconstrained multihead architectures. However, this des

arXiv:2610.21708v1 Announce Type: cross Abstract: When an LLM agent misbehaves, a human steps in, and that intervention is usually discarded or replayed as training data. We treat it instead as a meas

arXiv:2610.21789v1 Announce Type: cross Abstract: Existing safety benchmarks rely largely on synthetic prompts with loosely grounded taxonomies and offer little focused analysis of severe crime catego

arXiv:2610.21038v1 Announce Type: cross Abstract: Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed

I know I am probably missing the point of this benchmark, but I wanted to see if the model is capable of generating something better or is that the best it can do. So I gave it some encouragement. I f

arXiv:2610.21881v1 Announce Type: new Abstract: Training language models for formal theorem proving requires large numbers of verifier calls. Slow checks and timeouts often leave GPUs waiting and with

arXiv:2603.22519v4 Announce Type: replace-cross Abstract: Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the

arXiv:2610.21405v1 Announce Type: new Abstract: Long-term memory has become a crucial component of large language model (LLM) agents, with most research focusing on retrieving relevant information acc

arXiv:2610.21062v1 Announce Type: new Abstract: As large language models become increasingly capable of generating, and assisting with, scientific writing, there is a need for greater transparency and

arXiv:2610.20693v1 Announce Type: new Abstract: Video generators serve as interactive worlds for exploration and simulation, yet realistic and visually appealing frames do not necessarily depict a con

arXiv:2610.22158v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that use

arXiv:2610.21344v1 Announce Type: new Abstract: Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning mo

arXiv:2610.21040v1 Announce Type: new Abstract: Recursive Self-Improvement (RSI) has gained wide attention for its ability to work with limited data. In a proposer-solver loop, a proposer model writes

arXiv:2610.22293v1 Announce Type: cross Abstract: Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remain