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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.

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Mass General Brigham team publishes FAHR-Face study in The Lancet Digital Health, using facial photos to predict mortality risk in cancer patients

The U.S. Mass General Brigham health care system developed the FAHR-Face model, which identifies differences in mortality risk among cancer patients using only a single facial photograph; the study was published on October 20 in The Lancet Digital Health. Among 3,820 cancer patients who did not participate in training, 64 of the 122 people rated by the model as extremely high risk died within 90 days after the photo was taken, while only 20 of the 975 people with scores between 0 and 0.1 died, a rate of 2.1%. The study also conducted external validation using 4,892 patients at the Maastro radiotherapy center in the Netherlands and obtained similar results, but the model cannot yet be used directly in clinical practice.

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Morgan Stanley Releases 2027 AI Capital Expenditure Flow Chart: Nvidia Alone Captures Two-Thirds of S&P 500 Benefits

Morgan Stanley strategists Nick Lentini, Mike Wilson and others released the report "Tracking the $1.6 Trillion AI Money Flow," which for the first time traces hyperscalers' $1.58 trillion in 2027 capital expenditure line by line to specific companies' revenue. The report says AI capital expenditure will drive about 25% of S&P 500 sales growth, of which about two-thirds, or roughly $690 billion, flows to Nvidia, while the other 499 constituents together account for about $339 billion. The report also notes that hyperscalers have disclosed more than $1.1 trillion in lease commitments not yet commenced, which are economically equivalent to debt financing, and projects that 2027 will be the year with the largest free cash flow gap for them.

Anthropic says Claude traced the source of NIST elliptic curve seeds in half an hour

Anthropic published a study: an employee gave Claude's internal research model the question of the source of the NIST elliptic curve seeds, which had puzzled the cryptography community for more than twenty years, and Claude traced within less than half an hour that the seeds for P-192, P-224, P-256, P-384 and P-521 came from the lrand48 pseudorandom number generator in the C language library, with the initial state determined by the Unix timestamp. Anthropic said the finding did not break ECDSA and did not bring new attack methods, but it reduced the possibility that the relevant curves had been deliberately implanted with backdoors. Claude also traced that the base points of six curves including P-192 came from FreeLIP's random number generator, though the source of the B-163 curve seed and how the base points of some curves were generated remain unexplained.

Anthropic says Mythos 5 autonomously designed quantum circuits, surpassing human optimal solutions in 53 hours

Anthropic announced a study in which Mythos 5, given only natural language task descriptions, fully autonomously closed-loop designed 8 basic quantum circuits within 53 hours, all surpassing the best publicly published human designs. In the divsteps task in number theory and cryptography, it autonomously derived a new algebraic identity and completed formalization in Lean, reducing the number of quantum gates by 17 times while increasing qubits by only 12%; in the DQI decoding task, it switched to the Berlekamp-Massey decoding method and precisely calculated storage requirements, reducing gate count by 12 times compared with the original architecture. The research comes from Anthropic's STEM Fellows project, and the text also mentions that FeMoco simulation gate count dropped from the 50 trillion estimated by humans in 2017 to 340 million over 8 years.

Anthropic Research: How AI Is Changing Its Internal Hiring and Ways of Working

Anthropic has released an internal research report analyzing AI's impact on the company's hiring and work organization, based on data including hiring, AI usage, output metrics, employee surveys, calendars, and recruiting records, as well as interviews with 17 employees. The report says the share of junior employees in technical departments fell from 21% in early 2025 to 14%, and the share of junior technical roles in hiring dropped from 27% to 9%; by August, interactive AI Agents were working six times as many hours as employees, and per-engineer code commits reached five times the level of six months earlier. Human attention has become the bottleneck on Agent output, employees are shifting toward meta-work of building monitoring and orchestration Agents, and hiring now places more value on autonomy and judgment.

Imperial College London paper: search-augmented LLMs often cite web pages they never opened

Three authors from Imperial College London have published the paper "Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence," examining why search-augmented LLMs frequently cite web pages they never actually open. The study tested GPT-5.6 Luna and six open-weight configurations including GLM-5.3-Flash, gpt-oss-120b, gpt-oss-20b, gemma-4-31b, and qwen3-8b using 225 facts that changed after February 16, 2026, finding that models often answer based solely on search snippets without opening the source pages; under the same set of search results, the proportion of cases where no page was opened ranged from 2.6% to 76%, and when Luna used its built-in search, 53.6% of turns relied only on snippets. When answering from snippets alone, outdated answers accounted for 6.4% and misleading citations for 6.4%, whereas when pages were opened and cited, these fell to 1.3% and 2.3% respectively; placing the correct page directly into the prompt significantly reduced errors, but some models still favored old information even when shown the new facts.

DeepMind AlphaProof Nexus appears in《Science》: autonomously solves 9 Erdős problems

Google DeepMind's automated mathematical proof system AlphaProof Nexus appeared in the October 8 issue of《Science》, autonomously solving 9 of 353 open Erdős problems, two of which had remained open since 1970. The system uses Gemini 3.1 Pro to draft proofs in Lean, with a compiler verifying line by line and feeding back iterations; the reasoning cost per problem is several hundred dollars. The paper's ablation results show that a simplified system that removes complex structures such as the sub-agent tournament and retains only the large model plus Lean feedback can also solve these 9 problems, though the hardest few are more expensive.

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