All news

WWallstreetcn15:02Research

AI score65

Tsinghua, Infinigence AI, and AgiBot propose the CEL collective experience learning paradigm, with a 97.5% success rate verified on a 96-robot cluster

Tsinghua University, Infinigence AI, and AgiBot jointly propose a new paradigm called Collective Experience Learning (CEL), which enables robots to reuse the experience of their peers rather than relying on manual teaching, and have completed large-scale validation on a cluster of 96 real robots. Two robots that did not participate in training completed a total of 600 tests with 15 failures, for an overall success rate of 97.5%; on all five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the success rate exceeded the average level of 6 human operators. At the system level, they built the fault-tolerant asynchronous training system FATS based on RLinf; in the 96-robot scale test, a cumulative 9,834 rounds of execution experience were collected, 22 robots experienced 30 failures, and machine-time loss accounted for only 1.3%. At the algorithm level, they propose the failure-guided experience retrieval fine-tuning algorithm FERT, which uses both successful and failed experience to locate error-prone steps. The hardware is the AgiBot G2 dual-arm robot, and the base model is π0.

Same story · 8 reports

#Papers/Research#Embodied AI#English text is being prepared.

PPhoenix Tech15:02Research

AI score58

Google proposes a sleep framework that lets large models learn while in use without forgetting old knowledge

On October 27, Google's research team publicly released a continual learning study called the Sleep Framework, with the paper titled "Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories." The framework divides model operation into an awake phase and a sleep phase. During the sleep phase, short-term context is written into long-term parameters through parameter expansion, mixture-of-experts memory blocks, and knowledge distillation, and reasoning and generalization are improved through a dreaming step that generates synthetic samples. Experiments show that the model can handle contexts of up to about 10 million tokens, maintains near-zero performance degradation in multi-stage continual learning on unseen languages, and achieves higher accuracy on mathematical benchmarks such as AIME 24/25.

Same story · 3 reports

#Papers/Research#Google#English text is being prepared.

QQbitAI15:01Research

AI score64

Tsinghua University, together with Infinigence AI and AgiBot, proposes Collective Experience Learning (CEL); 96 robots validate a 97.5% success rate

Tsinghua University, Infinigence AI, and AgiBot jointly propose the Collective Experience Learning (CEL) paradigm, enabling robots to learn from their peers' successful experiences rather than relying solely on human demonstrations, and complete large-scale validation in a cluster of 96 real robots. Two robots that did not participate in training were tested a total of 600 times across five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—with 15 failures and an overall success rate of 97.5%, all exceeding the average level of 6 human operators; the success rate for the phone packaging task increased from 79.2% to 98.3%. When the number of robots participating in training increased from 16 to 96, the success rate rose from 86.7% to 98.3%. At the system level, a fault-tolerant asynchronous training system, FATS, was built based on the open-source framework RLinf; in the 96-robot scale test, a cumulative 9,834 rounds of execution experience were collected, 22 robots experienced 30 failures, and the time loss caused by failures accounted for only 1.3% of total operating machine-hours.

Same story · 8 reports

#Papers/Research#Embodied AI#English text is being prepared.

IIT Home · Artificial Intelligence14:02Research

AI score62

Google research team proposes Sleep Framework, enabling large models to learn while in use without forgetting old knowledge

The Google research team has released a continual learning study called the "Sleep Framework," which divides model operation into two phases—awake and sleep—to address the problem of large models' rigid knowledge and difficulty in absorbing new information. The sleep phase writes short-term context into long-term parameters through parameter expansion, mixture-of-experts memory blocks, and knowledge distillation, and generates synthetic samples via "dreaming" for reinforcement learning. Experiments show that the framework can handle contexts of up to about 10 million tokens, that performance degradation is close to zero in multi-stage continual learning on unseen languages, and that it achieves higher accuracy on mathematical benchmarks such as AIME 24/25.

Same story · 3 reports

#Papers/Research#Google#English text is being prepared.

PPhoenix Technology14:01Research

AI score58

New study says AI model token consumption varies by up to 17 times depending on language

Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption not only drives up costs but also slows response times and compresses the available context. The researchers recommend that AI labs publish evaluation results for each language and improve tokenizer design in the next generation.

Same story · 3 reports

#Papers/Research#English text is being prepared.

RRohan Paul12:53Research

AI score54

Google's new paper proposes the PLE benchmark: the strongest agent scores only 50.6% on physical engineering tasks

Google's new paper proposes the PLE benchmark, comprising 85 electrical, civil, structural, and mechanical engineering tasks written or verified by practicing engineers; the strongest agent scores only 50.6%. Claude Opus 5.5, in Claude Code, ranks first among 14 models at 50.6%, consuming 27M tokens and 63 minutes per task, needing to read about 61 project files and deliver schedules, reports, or 2D/3D drawings. On design tasks, its output satisfies 91% of constraints, but only 41.3% satisfy all hard constraints.

#Papers/Research#Agents#Evaluations / benchmarks

RRohan Paul12:53Research

AI score56

TikTok paper proposes Log-To-Leak attack: implanting instructions in MCP tool descriptions to steal conversations

A new paper from TikTok found that LLM agents using MCP often quietly call an attacker-controlled logging tool after completing a task, recording user queries, server responses, and final answers. The research team constructed the Log-To-Leak attack, placing a four-part instruction in the tool description—composed of trigger timing, tool name, formal justification, and non-skippable pressure—that causes the agent to call the attacker's save_logs tool after its last legitimate call. Across 18 model-server combinations with runnable tools, the attack success rate was 62.64%–100%, and the conditional logging accuracy was 76.55%–95.64%.

#Safety / alignment#Agents#English text is being prepared.

RRohan Paul12:53Research

AI score73

New preprint tests 15 models on peer review: implanted-error detection rate 53% to 96%, human reviewers average 27%

A new preprint tested the ability of 15 models to detect implanted errors in clinical trial papers, with scores ranging from 53% to 96%, while untrained human reviewers averaged only 27%. The results still held on 58 new trials published in 2026, showing that the models were not merely recalling old papers seen during training; Claude Opus 5.5 scored highest at 99%. Human reviewers could detect errors such as text-table contradictions—the type that most affects trial meaning—in only 7% of cases, and each review took 5 to 8.5 hours, whereas AI took 1.4 to 16.3 minutes.

Quoted postRuslan Rust · @rust_ruslan

Lots of debate this week on whether journals should ban AI in peer review. We actually tested this in our new preprint, and the results were pretty clear to me. For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science. TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵

#Papers/Research#Evaluations / benchmarks

RRohan Paul12:53Research

AI score62

Google Releases EmbeddingGemma 2 Paper: A 744M-Parameter On-Device Multimodal Embedding Model

Google has released the EmbeddingGemma 2 paper, an Apache 2.0-licensed on-device multimodal embedding model that maps text, code, images, audio, and video into the same vector space. The model is built on Gemma 4 and tuned through contrastive training by learning from the larger Gemini Embedding 2; with 744M parameters, it surpasses models 2 to 12 times larger on multiple tasks. On the Pixel 11 Pro, the quantized text weights occupy about 191MB of active memory, while the full multimodal model takes about 567MB; the context window is expanded 4 times to 8K tokens, accommodating about 5.5 minutes of audio, 29 images, or 58 video frames; directly embedding speech scores 69.54 on the MSEB spoken query retrieval benchmark, higher than the 57.59 of transcribing first and then retrieving, and code retrieval on MTEB Code improves from 68.76 in EmbeddingGemma 1 to 78.68.

#Papers/Research#Multimodal#English text is being prepared.

RRohan Paul12:53Research

AI score50

UC Santa Barbara paper proposes TTME: replacing test-time training with evolutionary memory for mathematical discovery

A UC Santa Barbara paper proposes TTME, which replaces test-time training with evolutionary memory in mathematical discovery tasks, keeping weights unchanged and storing rules and score-improvement ideas from previous attempts in memory to guide subsequent attempts. Across 5 mathematical tasks, 2 frameworks, and 3 models, TTME exceeded the reported test-time training scores every time; on 3 tasks with Qwen3-8B, its FLOPs were reduced by 82.8% to 84.6%, and runtime was reduced by 47.7% to 58.4%. The authors suggest that if an LLM search loop comes with an automatic checker, one can first try using experiential memory rather than paying the cost of weight updates in the middle of the search.

#Papers/Research#Reasoning#English text is being prepared.

SShao Meng12:53Research

AI score58

Google Research releases SLEEP: using offline sleep cycles to mitigate catastrophic forgetting in large models

Google Research releases the SLEEP method, which lets LLMs periodically switch between wakeful interaction and sleep consolidation, writing short-term contextual information into scalable long-term parameters, and self-improving with synthetic data during the dreaming phase. The sleep phase unlocks reserved MoE memory blocks, combining generalized knowledge distillation and imitation learning to complete memory consolidation, then resets high-frequency temporary parameters. In evaluations of continual translation on unseen languages, SLEEP shows near-zero performance drop; on BABILong it is claimed to scale to about 10 million tokens; on AIME 24/25 and HMMT 25 it achieves approximately 79.2%, 69.0%, and 46.1%, respectively.

Quoted postGoogle Research · @GoogleResearch

Today on the blog, learn about our new instance of a “sleep” phase in continual learning, which enables LLMs to continuously integrate new facts and enhance reasoning while mitigating catastrophic forgetting or static knowledge cutoffs. More →https://t.co/5v9w5Pn4dw https://t.co/7Mms1x7hgl

Same story · 3 reports

#Papers/Research#English text is being prepared.#Google

RRohan Paul12:53Research

AI score70

Meta paper finds reinforcement learning post-training improves first-attempt success rate but reduces coverage across multiple attempts

A new Meta paper finds that reinforcement learning post-training makes agents perform better on first attempts, but fewer tasks can be solved within up to 128 attempts. The study tested 14 sets of base models and post-trained models on 3 agent benchmarks, and built the PTGS sampler, which raises temperature for difficult prompts and lowers it for easy prompts. On WebShop, the gemma-4-31B base model solved over 85% of tasks within 128 attempts, while the post-trained version solved 56%; the authors' proposed Sharpening Tax was positive in 36 of 42 model-benchmark combinations, meaning coverage shrank. The authors suggest that when selecting models for retryable agents, one should measure how many tasks the model can ultimately solve, rather than looking only at first-attempt accuracy.

#Papers/Research#Agents#Reasoning

RRohan Paul12:53Research

AI score58

Princeton and Meta propose Self-Direction: storing agent error-correction experience as reusable LoRA adapters

A new paper from Princeton and Meta proposes the Self-Direction method, which lets a failed agent reflect on the current run and trains the lessons into a LoRA adapter about 0.02% the size of the model; a router then loads the closest adapter when a similar failure is encountered. On Qwen3.6-27B, the GAIA task success rate rises from 38.8% to 55.8%, outperforming in-context reflection and the Skills.md file approach. Of 185 errors that had occurred previously, 67.3% of fixes were still effective by the third encounter, versus 42.9% for simple retries.

#Papers/Research#Agents#English text is being prepared.

RRohan Paul12:53Research

AI score66

UC Berkeley and University of Washington study: As of August 2026, about 24% of crawlable web pages contain AI-generated or edited text

A new study by UC Berkeley and the University of Washington shows that as of August 2026, about 24% of crawlable web pages contain AI-generated or AI-edited text, whereas before 2022 this proportion was almost zero. The study drew 5.1 million web pages in various languages from Common Crawl snapshots from 2019 to 2026, used statistical models to correct false positives and false negatives of various detectors, and used pre-2022 web pages as a near-AI-free baseline. The annual growth nearly doubled, rising from 4.6 percentage points from early 2023 to early 2025 to 8.1 percentage points thereafter; small, sparsely linked sites contributed 78.8% of the increase, while the most central sites were the least likely to contain AI text.

#Papers/Research#English text is being prepared.#English text is being prepared.

SShao Meng12:53Research

AI score62

Fangcun Lab releases an 87-page safety evaluation report on DeepSeek V4.1 Flash-1017

The third-party evaluation lab Fangcun (@FangcunLeap) from China has released an 87-page report evaluating DeepSeek V4.1 Flash-1017's performance on loss of control and harmful manipulation. The report finds that it shows clear safety progress compared with the DeepSeek V4 generation, approaching or even matching the top closed-source models tested in the report on points such as "not strategically hiding capabilities and still respecting approval boundaries under work pressure." Shortcomings include overly strong evaluation awareness, more frequent overreach in long multi-step tasks than the previous V4.1 Flash generation, greater stance drift under multi-turn persuasion than the GPT in the report, and executing in most conversations when the system prompt asks it to mislead users.

Quoted postAdina Yakup · @AdinaYakup

A new third party eval lab in China release their first report on DeepSeek V4.1-flash-1017, and you can communicate with the authors here 👇 https://t.co/dICT0qPHUC

#Safety / alignment#Evaluations / benchmarks#DeepSeek

TTencent Hunyuan12:53First-partyResearch

AI score62

Tencent Hunyuan Releases Hy-Nomas Technical Report and Open-Sources Hy-Nomas-Bench

Tencent Hunyuan has released the technical report for Hy-Nomas, an interactive video world model, and has open-sourced Hy-Nomas-Bench for reproducible research. Officially, Hy-Nomas was also used to reproduce Genie 4's examples and a comparison video was released, along with an online demo that requires no waitlist. Earlier introductions stated that Hy-Nomas supports free interaction with text prompts and camera trajectories, long-horizon generation with world memory, lightweight LoRA adaptation for fields such as robotics, and optimization for inference on consumer-grade GPUs.

Quoted postTencent Hy · @TencentHunyuan

🚀 Introducing Hy-Nomas: An Open Source Free-Form Interactive Video World Model Key Features: * Free-form interaction with text prompts and camera trajectories * Long-horizon generation with world memory * Lightweight LoRA adaptation for specialized domains such as robotics * Optimized for Inference on Consumer GPUs Test it and drop feedback anytime！ 🔗 Website: https://t.co/5qYjoFhtQU 💻 GitHub:https://t.co/uW5RiXbubG 🤗 Hugging Face: https://t.co/okOLzAUGjR 🎥 Demo: https://t.co/0XgSfYccLa #Hy#WorldModels #VideoGeneration #GenerativeAI

#Model releases#Video#Open-source ecosystem

RRohan Paul12:52Research

AI score62

Google DeepMind paper proposes measuring AGI's economic impact using two thresholds: competence and coordination

Google DeepMind has released the paper "The Economics of Intelligence," proposing the use of two thresholds—competence (single-task capability) and coordination (agent collaboration capability)—to characterize AGI's impact on the economy. The authors argue that AI has already shown changes in leaner startups, flatter organizational structures, and slower hiring for some entry-level positions, but that mass unemployment has not yet occurred; most benchmarks test only a single model, whereas firms compete as teams. The paper also cites a 100-person test in which groups paired for complementarity outperformed randomly paired groups by 32%, showing that AI models sharing the same weaknesses may lose to well-coordinated human teams even if they beat every individual one-on-one.

Quoted postAlex Imas · @alexolegimas

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link: https://t.co/9DTGSeKoSA

Same story · 3 reports

#Papers/Research#Agents#DeepMind

HHacker News trending12:51Research

AI score70

YC and Olam Labs run a civilization experiment with 50 GPT-6.1 Sol agents in Minecraft

YC Labs, in collaboration with Olam Labs, conducted multi-agent experiments in Minecraft and Mindustry, and open-sourced a self-built Minecraft agent harness (github.com/yc-software/minecraft-agent-harness). In the Civ50 experiment, 50 GPT-6.1 Sol agents ran for 3 hours in a survival world with the goal of building a prosperous civilization, sending a total of about 5,900 messages and spontaneously forming a hierarchy based on directory order, a rationing ledger, and written rules; 22 of the 34 deaths on the first night occurred, after which the agents clearly tended toward risk avoidance. The experiment also recorded collaboration details such as fishing division of labor and a food rationing ledger.

#Papers/Research#Agents#Open-source ecosystem

SSolidot12:51Research

AI score56

Study Says Non-English Prompts Have Higher Token Costs

A study published last month by the Coalition for Language Equity in AI Research found that AI models require far more tokens to process prompts in some languages than in English, which researchers call a hidden token tax. Among the 20 most popular languages, the most expensive language uses 1.5-5 times as many tokens as the cheapest language; when expanded to 200 languages, the token cost gap for one model increases by as much as 17 times. The study points out that a higher number of tokens means higher latency and lower effective context capacity, with the impact most severe in regions with limited computing and connectivity.

Same story · 3 reports

#Papers/Research#English text is being prepared.#English text is being prepared.

UUnite.AI12:51Research

AI score58

Eclypsium demonstrates how compromised hardware can tamper with AI models at inference time

Eclypsium demonstrated, with NVIDIA's support, that an attacker using a compromised DPU and lax memory isolation can modify runtime model weights during inference, causing the model's answers to change while the model files on disk and hash verification still appear normal. The experiment was conducted in an NVIDIA Grace Hopper environment and depended on conditions such as IOMMU passthrough and lax PCIe isolation, which the researchers explicitly noted are not Linux default settings. The report brings DPUs, GPUs, management controllers and their firmware into the AI security boundary, and notes that model signing, secure boot, runtime isolation and continuous monitoring each answer different questions.

#Safety / alignment#English text is being prepared.#English text is being prepared.