2026-06-06

Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

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06/04 08:00

Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolv

ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?

👍 40

06/04 08:00

Role-playing language agents (RPLAs) should play characters whose values and behavior evolve as the story progresses, not maintain a fixed persona. Existing benchmarks measure factual recall at a given chapter, not whether responses align with the character's psychological trajectory, especially in

TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration

👍 36

06/03 08:00

Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface only the problems the user has noticed, while many other important problems coexist, hidden in plain sight, within the broader user context, with their

AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints

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06/04 08:00

Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual c

VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding

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06/03 08:00

We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-orien

Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation

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06/04 08:00

Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To tra

RobotValues: Evaluating Household Robots When Human Values Conflict

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06/02 08:00

While household robots are often evaluated based on task completion, everyday domestic environments involve value-conflicting situations in which robots are expected to choose actions that prioritize other values than task success, such as human autonomy, efficiency, or social appropriateness. Yet,

Personal AI Agent for Camera Roll VQA

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06/03 08:00

We study the personal camera roll visual question answering setting. In this setting, a conversational AI assistant can access a user's personal camera roll and retrieve relevant photos to answer queries, ranging from simple factual questions (e.g., ``Name of the food I tried yesterday?'') to more o

LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing

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06/04 08:00

Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier field. Existing unified frameworks predominantly rely on massive models (typically 13B parameters or more) and incorporate source video conditions for

Rethinking Continual Experience Internalization for Self-Evolving LLM Agents

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06/03 08:00

Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under m

Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?

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06/04 08:00

Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domain. A natural question follows: how well do these models reflect the physical world when their generated videos leave the screen and enter reality? W

Unsupervised Skill Discovery for Agentic Data Analysis

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06/04 08:00

Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success cri

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

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06/04 08:00

Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use. We introduce PropMe, a propensity-aware framework for memorization evaluation that contrasts prefix-based

MAOAM: Unified Object and Material Selection with Vision-Language Models

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06/02 08:00

Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection region through either text or click-based interactions, and the system should support selecting not only objects but also other criteria, such as mater

The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs

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06/02 08:00

Inference-time scaling has emerged as a critical avenue for enhancing Large Language Models' performance, yet real-world deployment is constrained by strict computational budgets. In this work, we formulate inference budget allocation as a global constrained optimization problem governed by economic

Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents

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05/28 08:00

Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

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06/04 08:00

We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the world modeling interface to learn from extensive egoce

Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction

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06/04 08:00

Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in text space: once visual evidence is verbalized, fine-grained motion, geometry, and interaction cues can be lost, leading

Towards One-to-Many Temporal Grounding

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06/04 08:00

Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal

MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

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06/04 08:00

Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution becomes a key capability. However, existing MLE agents suffer from inter-branch information isolation, memoryless searc

SpaceX IPOs in 7 days. I Fed the S1 Doc Into Claude. Here Is What It Found Buried in 300 Pages.

@DamiDefi · 96.5K 粉丝 · 2.3M 阅 · 584 赞 · 80 转

06/04 17:31

The number that stopped me was not the $2 trillion valuation. It was $791 million. That is what SpaceX made in net income in 2024. A profitable, growing aerospace company with a genuine moat in launch

How to master Dynamic Workflows in Claude Code: 6 patterns and 14 steps Anthropic engineers actually

@0xCodez · 3.3K 粉丝 · 637.2K 阅 · 510 赞 · 59 转

06/03 19:00

Most Claude Code users still write their workflows by hand. They chain prompts, copy outputs, paste them into the next prompt, fix what went wrong, repeat. 9 out of 10 builders haven’t tried Dynamic

Generative UI Is the New Frontend

@Saboo_Shubham_ · 116.2K 粉丝 · 263.3K 阅 · 517 赞 · 74 转

06/04 01:12

The frontend used to be a fixed thing. Designers drew it. Engineers built it. Users got what shipped. That's over. The interfaces shipping in 2026 are drawn partly by the agent itself, in real time,

A guide to /goal 🥅

@dkundel · 19.3K 粉丝 · 116.9K 阅 · 523 赞 · 40 转

06/05 05:39

We launched the goal mode (or /goal) as a way to help you have Codex drive towards a concrete outcome. When you set a goal Codex will continue to work until the goal is achieved, whether that takes

A Functional Taxonomy of World Models

@drfeifei · 738.0K 粉丝 · 72.2K 阅 · 699 赞 · 144 转

06/04 02:57

“The world is everything that is the case.” — Ludwig Wittgenstein, Tractatus Logico-Philosophicus, 1921 The world is not made of words. In an earlier essay, we argued that spatial intelligence is AI’s

How to Build a Custom Agent Harness

@sydneyrunkle · 7.5K 粉丝 · 69.5K 阅 · 511 赞 · 74 转

06/04 00:57

Building useful agents is largely about customization: connecting your agent to the right context, data, and environment(s) for the task at hand. At its core, an agent is a model calling tools in a

Your token spend is an AI architecture problem, not just a model problem

@jainarvind · 9.3K 粉丝 · 53.7K 阅 · 505 赞 · 68 转

06/04 06:04

Enterprise AI token spend is scaling quickly, especially as the technology shifts from simple chat assistants into coding agents, AI coworkers, and long-running workflows. These systems do far more

10 HERMES AGENT HACKS THAT TURNED MY CHAT AGENT INTO A 24/7 SYSTEM

@IBuzovskyi · 1.2K 粉丝 · 50.1K 阅 · 500 赞 · 51 转

06/03 17:16

These 10 Hermes Agent hacks saved me 15+ hours every week - and they work for any workflow you run repeatedly. Content, software development, business operations, client management, research, sales.

Feedback loops: Help Claude Code complete ambitious tasks with less babysitting

@delba_oliveira · 74.0K 粉丝 · 37.4K 阅 · 533 赞 · 38 转

06/04 00:04

As we delegate more ambitious tasks to Claude, it becomes increasingly important that it can verify its own work. The more Claude can self-verify: the more independently it can work on long-running

Don't let your agent guess, give it runtime context

@ericzakariasson · 67.9K 粉丝 · 37.3K 阅 · 507 赞 · 27 转

06/03 23:45

If you've ever watched an agent try to fix a bug, you've watched it guess. It reads the code, comes up with a theory, makes an edit, and hopes. Sometimes it's right. A lot of the time you get a fix

Thousand Token Wood: shipping a multi-agent economy on a 3B model

Hugging Face Blog · 06/06 06:18

How to Stop Shipping Low-Quality RL Environments (with Examples)

Latent Space · 06/06 02:49

Your broken harness is actively making the model worse. Here's what I keep seeing after years of eyeballing trajectories, and what you need to fix.

The Meta hack shows there’s more to AI security than Mythos

MIT Tech Review AI · 06/05 17:00

On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama Wh

[AINews] not much happened today

Latent Space · 06/05 14:44

a quiet day

Anthropic Oceanus leaks 🤖, ChatGPT Dreaming 💭, recursive self improvement 🚀

TLDR AI · 06/05 08:00

Reality: The Final Eval — Lukas Petersson and Axel Backlund of Andon Labs

Latent Space · 06/05 04:39

We talk with the VendingBench authors on evaling Claudes from Haiku to Mythos, and how they build leading, and lasting, frontier evals from scratch.

Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI

Hugging Face Blog · 06/05 02:57

EVA-Bench Data 2.0: 3 Domains, 121 Tools, 213 Scenarios

Hugging Face Blog · 06/04 20:24

How Endava is redesigning software delivery around AI agents

OpenAI News · 06/04 20:00

Learn how Endava is using AI agents, ChatGPT Enterprise, and Codex to accelerate software delivery, automate workflows, and build an AI-native culture across the enterprise.

How courts are coping with a flood of AI-generated lawsuits

MIT Tech Review AI · 06/04 18:50

Most days in her chambers, Judge Maritza Braswell, a federal magistrate judge in Colorado, sifts through stacks of documents written by people without a lawyer. Many of them can’t afford to hire a lawyer, and others have cases too weak or too small to interest one. She reads each one carefully, mind

Dreaming: Better memory for a more helpful ChatGPT

OpenAI News · 06/04 17:00

ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.

not much happened today

Smol AI News · 06/04 13:44

**NVIDIA** released **Nemotron 3 Ultra**, a fully open **550B MoE** model with **55B active parameters** and **1M context**, optimized for long-running agent tasks with up to **5x speedup** and **30% cost reduction**. It features hybrid Mamba/attention, LatentMoE, native MTP, and was pretrained on *

[AINews] Reve 2 and Ideogram 4: Layouts in Imagegen

Latent Space · 06/04 11:24

a quiet day.

Biodefense in the Intelligence Age

OpenAI News · 06/04 08:00

An action plan for AI-powered biological resilience

Designing the hf CLI as an agent-optimized way to work with the Hub

Hugging Face Blog · 06/04 08:00