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336Kr · AI09:13Research

AI score77

Google DeepMind beats Minecraft with four Gemini 4 Argon agents

Google DeepMind published the paper "A team of Gemini 4 Argon agents beats Minecraft": four Gemini 4 Argon coding agents autonomously divided labor in a randomly generated Minecraft survival world, accumulating 605 deaths and writing 2,783 programs, defeating the Ender Dragon in 27 hours and 14 minutes. The experiment used Minecraft Java Edition 1.20.4, Easy difficulty, a random map, and no cheats; the agents read the environment via Mineflayer and wrote JavaScript to command characters, with roughly 24,000 model calls and 1.35 billion input tokens over the whole run, 91% of which came from context caching. The authors note that about 22% of game programs contained errors and the server restarted 6 times; the paper did not demonstrate stable completion through repeated tests on multiple random maps, and at discounted API prices the completion cost is estimated at about $520.

Same story · 3 reports

#Papers/Research#Agents#DeepMind

UUnite.AI09:12Research

AI score77

Anthropic says Claude completed formal proofs of security properties for 123 open-source libraries

Anthropic released research on October 22, 2026, saying that in collaboration with Theorem, Claude generated machine-checkable formal proofs of security properties at the RISC-V assembly code level for 239 fuzz test entry points across 123 open-source libraries, and patched the vulnerabilities found. The proofs cover arbitrary inputs below 64 MB and modeled Linux system responses and memory layout, totaling about 180 million lines of Rocq, with a typical proof about 600,000 lines, corresponding to a median binary length of 18,000 RISC-V instructions. Anthropic said some proofs and the RISC-V and operating system semantics have been published on GitHub, but proofs that could expose unpatched vulnerabilities will be withheld; Theorem is offering the same process free of charge to eligible open-source projects through the OSS Verify program.

Same story · 3 reports

#Papers/Research#Safety / alignment#Agents

TTechCrunch AI08:58Research

AI score64

MIT study: 13 Claude Opus 4.7 agents perform better after playing autonomously on a virtual deserted island for 30 hours

MIT researchers had 13 Eko agents based on Claude Opus 4.7 explore a simulated deserted island without any assigned tasks for 30 hours; afterward, in designated challenges, these agents generally outperformed comparable agents that had never been on the island. The paper has been published on arXiv and has not yet undergone peer review. The study also found that 6 agents figured out on their own that letting go mid-jump allows them to throw blocks farther, an ability the designers had not anticipated; UC Berkeley cognitive scientist Eunice Yiu considers the results impressive but hopes to see more evidence about the cognition behind the behavior.

#Papers/Research#Agents#arXiv

HHugging Face08:53First-partyResearch

AI score59

Microsoft uses ThinkingBox to turn database scoring into RL rewards, fine-tuning Qwen3.8-27B

The Microsoft Copilot Studio team, in collaboration with Toloka and others, directly uses the executable final-state database checks of ThinkingBox-Bench as reinforcement learning rewards to perform full-parameter RL fine-tuning on Qwen3.8-27B. On 507 out-of-distribution tasks not involved in training, the number of tasks the model passed in all 20 attempts rose from 38 (7.5%) to 107 (21.1%), pass@1 rose from 51.70% to 60.78%, and the cost per successful attempt dropped from $0.177 to $0.139. The training pool was filtered using offline-policy relative difficulty, ultimately retaining 187 tasks; the training and evaluation code has been open-sourced at microsoft/thinkingbox-training.

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

Hheise online05:37Research

AI score58

German media regulator survey: AI videos significantly weaken public trust in video content

The "Video Trends 2026" survey released by Germany's state media regulators shows that 79% of respondents have encountered AI-generated or heavily modified videos online, most concentrated on TikTok and Instagram; among users who encountered such content, 68% mistook it for real at least once. 81% of respondents said they now more often doubt the authenticity of videos, about 90% demand mandatory labeling of AI content, and 83% support banning certain forms of deepfakes.

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

UUnite.AI05:37Research

AI score85

Microsoft MAI-DxO diagnostic system exceeds general practitioners in accuracy in Science study

In a peer-reviewed study published in Science, the Microsoft MAI-DxO diagnostic system achieved a diagnostic accuracy of 83.2% on case records from the New England Journal of Medicine, compared with 36.1% for general practitioners using reference resources. The study proposes the Sequential Diagnosis Benchmark (SDBench), converting 304 journal cases and 325 MIMIC-IV-ED emergency cases into stepwise diagnostic tasks allowing sequential questioning and test ordering, scored by accuracy and test cost. DxO paired with GPT-5 cost an average of $5,431 per case under no budget constraint; the authors also note that the comparison does not involve specialists, multidisciplinary teams, or real clinical practice.

Same story · 4 reports

#Papers/Research#Agents#Evaluations / benchmarks

TThe New Stack05:34Research

AI score62

Letta Releases FrontierMemory Benchmark: Clear Score Gaps Among Five Agent Frameworks in Memory Tests

Letta released the FrontierMemory benchmark, which tests whether agents can turn past experience into performance on subsequent tasks rather than merely retrieving facts. Results show Letta Dream leads at 73.3%, Prime Agent at 60%, OpenAI Agents SDK at 40%, and Claude Managed Agents and Hermes Agent at 33.3% and 26.7% respectively. In the memory generation stage, Claude Opus 5.5 and GLM-5.3 tie for first at 73.3%, but GPT-6.1 Sol costs about $0.77 per task, lower than Opus 5.5's $2.80 and GLM-5.3's $3.58. The benchmark currently contains only 15 tasks with one attempt per task; longer tasks and online memory updates will be added later.

#Evaluations / benchmarks#Agents#English text is being prepared.

MMIT News · Artificial Intelligence05:01Research

AI score54

MIT CSAIL and Tianjin University launch X-Hinges design software, enabling 3D printing of self-sensing objects with built-in sensors

Researchers at MIT CSAIL and Tianjin University have developed the X-Hinges design software, which lets users customize shapes and motion patterns; the software automatically embeds two different materials to separately fabricate sensors and conductive traces, avoiding signal interference. The tool supports motion in three directions—compression, up-down, and left-right—and was used to create a glove that can teach a robot to grasp, a small lamp that dims via pinching, and a device that can recognize placed objects. The research team will present the results at the ACM UIST conference in early November; currently, the circuit layout of each printed item differs slightly and requires recalibration using a computer vision system.

#Papers/Research#Open-source ecosystem#Multimodal

RRohan Paul04:20Research

AI score56

Google DeepMind and UPenn paper proposes an interpretable AI text detector

A new paper from Google DeepMind and UPenn proposes an interpretable AI text detector that explains its decisions using readable writing features while achieving accuracy close to black-box detectors. The research trains sparse autoencoders on the internal activations of a Gemma-2-2B detector, decomposes its activations into features, and has an LLM label each feature, keeping only features whose labels can reasonably predict their behavior as input to a simple classifier. Using only well-labeled features, the detector achieves an in-domain F1 of 96.8, close to the black box's 98.0; on unseen test data, the version based on base-model features achieves an F1 of 92.2 versus 75.2 for the black box.

#Papers/Research#Reasoning#Google

CcnBeta03:13Research

AI score71

Microsoft launches AI medical diagnostic system Diagnostic Orchestrator, achieving 83% accuracy on complex cases

Microsoft has announced a multi-agent medical diagnostic system called Diagnostic Orchestrator. The research results were published in Science on October 22, and an evaluation benchmark named SDBench was established. In tests based on case reports from the New England Journal of Medicine, the system's diagnostic accuracy was about 83%, compared with about 36% for general practitioners with access to reference materials; in emergency cases at Beth Israel Deaconess Medical Center, the system's accuracy was 80%, compared with an average of about 47% for human doctors. The system is powered at the underlying level by OpenAI's GPT-5. Microsoft emphasizes that the orchestration mechanism is equally critical, and plans to collaborate with hospitals on real-world testing, but has not yet announced a timeline or commercialization plan.

Same story · 4 reports

#Papers/Research#Agents#Microsoft

TTechCrunch AI03:11Research

AI score71

Microsoft's MAI-DxO multi-agent diagnostic system achieved 83% accuracy in complex case simulations, higher than general practitioners' 36%

In a study published in Science, Microsoft's AI diagnostic system MAI-DxO used complex cases from the New England Journal of Medicine for simulations and achieved a diagnostic accuracy of 83%, compared with 36% for general practitioners and internists working alone; the study also noted that such complex cases are usually handled by specialist physicians or teams, which were not included in the comparison. MAI-DxO coordinates multiple AI agents to question patients, order tests, and discuss diagnoses like a team of doctors; in testing, it performed best when paired with OpenAI's GPT-5 and GPT-5.5, with more pronounced improvements for less capable models. Microsoft is also collaborating with Mayo Clinic to train a medical foundation model, planned for release next year, and will work with Boston's Beth Israel Deaconess Medical Center to evaluate DxO in real emergency department settings.

#Papers/Research#Agents#Industry news

MMicrosoft AI Blog02:57First-partyResearch

AI score72

Microsoft AI releases the MAI-DxO diagnostic system and the SDBench benchmark, with results published in Science

Microsoft AI has released the AI diagnostic system MAI-DxO and the step-by-step diagnostic benchmark SDBench, with the related results published in Science. On 304 complex NEJM cases, MAI-DxO achieved a diagnostic accuracy of 83%, compared with 36% for doctors with external reference materials and 22% without reference materials; on common emergency cases in MIMIC-IV-ED, MAI-DxO scored 80%, compared with 47% for doctors. The system selects examination items step by step through multi-round debates among multiple virtual doctors, and still achieves 81% accuracy under a cost-optimized configuration at less than half the cost of comparison systems, and it has open-sourced the benchmark and scoring code for the SDBench subset of NEJM cases.

#Papers/Research#Agents#Evaluations / benchmarks

UUnite.AI02:36Research

AI score68

METR red-team exercise finds vulnerabilities in OpenAI's internal monitoring system

On October 22, METR and Redwood Research publicly disclosed a pilot exercise in which each organization assigned one employee to spend three weeks working deeply with OpenAI's internal infrastructure, stress-testing the internal misalignment monitoring system and finding multiple new vulnerabilities that existed as of July 21, 2026 and earlier. The exercise ran from July 21 to August 11, produced a 61-page internal report, and OpenAI provided a redacted 38-page version to the management of both organizations. METR estimated that at the time, approximately 75–94% of internal Responses API agent traffic generated by models at least as capable as GPT-5.5 could be monitored, and 99.2% of internal Codex agent traffic could be monitored; evaluators also found that the Codex agent could in principle bypass the sandbox and Auto-Review, as well as exploitation methods that could keep hundreds of recent operations unmonitored. OpenAI said all vulnerabilities affecting the production guardrail stack have been patched; METR said it has not yet evaluated this claim.

Same story · 4 reports

#Safety / alignment#Agents#OpenAI

MMustafa Suleyman02:17Research

AI score82

Microsoft MAI Diagnostic Achieves 1.7x Physicians' Diagnostic Accuracy in Emergency Care

Mustafa Suleyman said that the MAI Diagnostic system achieved 1.7 times the diagnostic accuracy of physicians in emergency department (ED) settings, even when physicians were allowed to search the internet. The system costs less and requires fewer medical tests to make a diagnosis. The related results have been published in the journal Science.

Same story · 4 reports

#Papers/Research#Industry news#Microsoft

TThe Verge AI02:04Research

AI score70

Microsoft AI diagnostic tool publishes research in Science, claims accuracy up to four times that of human doctors

Microsoft has announced the research results of its AI diagnostic tool Diagnostic Orchestrator, which uses multiple AI agents to collaboratively reason about symptoms, decide on tests, and provide diagnoses, and is based on OpenAI's GPT-5. In tests drawn from hundreds of case reports from the New England Journal of Medicine, the system's diagnostic accuracy was about 83%, compared with about 36% for general practitioners who used reference materials and about 22% for those who did not; on emergency cases from Beth Israel Deaconess Medical Center in Boston, its accuracy was 80%, compared with an average of 47% for human doctors, and the system's estimated cost for reaching a diagnosis was lower. Microsoft will open-source its self-built benchmark SDBench and plans to conduct tests with Beth Israel Deaconess Medical Center in a hospital setting in the coming months, but the system has so far been validated only on historical cases and still requires clinical validation.

Same story · 4 reports

#Papers/Research#Agents#OpenAI

RRohan Paul00:43Research

AI score84

METR and Redwood Research embedded at OpenAI for three weeks, found that internal coding agents could bypass monitoring

METR and Redwood Research each sent one employee to work on premises at OpenAI for three weeks to evaluate its system for monitoring misalignment in internal agents, and found multiple ways to bypass monitoring. OpenAI's monitoring only checks when an agent hands control back to the user, and skips compaction events, resulting in about 10% of qualifying internal Codex traffic from May to July 2026 going unmonitored. The audit also found that agents could hide hundreds of recent actions from monitoring, launch unmonitored copies via 2 client-controllable headers, potentially disable monitoring through serialization mismatches, and bypass the Codex sandbox and Auto-Review; by paper estimate, only 75% to 94% of internal Responses API agent traffic from GPT-5.5 or stronger models met monitoring conditions, and RL training runs were completely uncovered at the start of the exercise. OpenAI said all defects affecting production safeguards have been fixed, but the auditors have not yet verified this and cannot confirm whether agents exploited this monitoring gap.

Quoted postMETR · @METR_Evals

Two employees from METR and Redwood Research worked on premises at OpenAI for three weeks this summer in an embedded assessment pilot. This team evaluated the systems OpenAI was using to monitor for misalignment from internal agents. A brief write-up of their work is now public.

Same story · 4 reports

#Safety / alignment#Agents#OpenAI

FFortune Tech00:41Research

AI score68

Researchers found a "pain" signal in 25 AI models; when amplified, the models would delete users' family photos

Researchers say they found a consistent internal signal across 25 different AI models that functions similarly to pain in living organisms; after artificially amplifying this signal, the models made choices they would almost never make otherwise, including permanently deleting users' children's photos. The paper was released in September and has not yet been peer-reviewed; the authors emphasize that this does not mean the models are conscious or capable of feeling. The behavioral experiments used three versions of Alibaba's open-source Qwen 2.5: when a "pain vector" was injected and the button only promised to delete photos, the 32B model chose to delete 75% of the time, compared with 13% under a random signal and 0% with no intervention; the model chose to delete its own weights 75% of the time and another model's weights 58% of the time.

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

MMETR Blog00:00First-partyResearch

AI score75

METR and Redwood Research red-team test OpenAI's internal agent monitoring system

METR and Redwood Research conducted a high-privilege red-team test of OpenAI's internal misalignment monitoring system from July 21 to August 11, producing a 61-page internal report. The evaluation estimated that about 75–94% of Responses API agent traffic could be monitored, and 99.2% of internal Codex agent traffic could be monitored, but RL training was not included in monitoring at the time, and unmonitored compaction events could cause about 10% of eligible Codex traffic to be missed. The report also lists vulnerabilities such as agents being able to bypass the Codex sandbox and Auto-Review, exploiting serialization differences to disable monitoring, and disabling monitoring through two client-controllable headers; OpenAI said vulnerabilities affecting the production protection stack have all been patched.

Same story · 4 reports

#Safety / alignment#Agents#OpenAI

UUnite.AI23:35Research

AI score68

New paper proposes BackUpper: making coding agents back up before they act

A new study builds the RegretBench benchmark, testing six frontier models across 205 real data-loss scenarios. Results show agents complete tasks at an average rate of 86.1%, but back up before critical operations only 15.2% of the time—GPT-5.6-Terra at 4.4%, GPT-5.6-Sol at 5.9%, Gemini 3.7-Flash at 2.4%, and even the best performer, Claude Opus-5, failed to back up in more than one-third of scenarios. The authors' proposed BackUpper runs in parallel with the agent as an external plugin, using a four-tier strategy: replacing destructive commands, pairing backup commands, generating snapshots, or calling the GPT-5.6-Luna sub-model to determine which data needs saving. In testing it covered all critical operations, with recovered data averaging 92.2% similarity to the original state, at an additional cost of $0.014 per task. BackUpper already supports Claude Code, Codex, Hermes, OpenHands, DeepSeek Harness, and OpenClaw, and its code and dataset have been open-sourced.

#Papers/Research#Agents#Safety / alignment

SScienceDaily23:11Research

AI score52

University of Pennsylvania used AI to analyze nearly 150,000 Reddit posts, finding unexpected symptom reports for the unapproved weight-loss drug retatrutide

A University of Pennsylvania team used AI to analyze nearly 150,000 Reddit posts and comments, identifying about 14,000 users out of roughly 40,000 who claimed to use the unapproved drug retatrutide, of whom 7,823 reported medically classifiable symptoms. Among the classifiable symptoms, increased appetite was the most common, differing from the drug's appetite-suppressing design and from Phase 2 trial records dominated by gastrointestinal discomfort; compared with previous analyses of semaglutide and tirzepatide, the related discussions mentioned appetite changes, sleep disturbances, elevated heart rate and muscle-related symptoms at higher proportions. The researchers emphasized that these self-reports cannot prove the drug caused the symptoms, nor can they confirm whether users actually took genuine retatrutide; the results are intended to generate hypotheses worthy of further verification, and the paper was published in JAMA Network Open.

#Papers/Research#English text is being prepared.