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#DeepSeek

All collected and published items, ordered by time. Picks are a selected subset.
Research

Research papers, technical reports, benchmarks and datasets.

10/15Thu
  1. The Decoder81

    GPT-6-Astra solves 49 of 50 physics research problems at $2.40 apiece

    GPT-6-Astra, running in OpenAI's Codex environment without human hints, solved 49 of 50 TheoPhysEval problems across eleven theoretical physics subfields, costing just under $116 overall. Anthropic's Opus 5.5 scored 92 percent, while both models reached 92 percent without internet access. The researchers emphasize that the benchmark tests calculations using known methods, while choosing promising research questions remains a separate challenge for current agents.

    Why it matters: The benchmark distinguishes proficiency in concrete physics calculations from the ability to choose fruitful research questions, clarifying which parts of scientific work its high scores actually measure.

10/14Wed
  1. Artificial Analysis · X55

    AA-AgentPerf adds DeepSeek V4.1 Flash benchmarks across NVIDIA and AMD hardware

    Artificial Analysis added DeepSeek V4.1 Flash to AA-AgentPerf, its benchmark replaying real agentic coding sessions with prompt caching and tool-call delays. At 100 tokens/s per user, GB300 leads with approximately 261M tokens/s per MW, followed by B300 at approximately 220M and MI355X at approximately 128M. Compared with DeepSeek V4 Pro 0813, V4.1 Flash scores 39 versus 36 on the Artificial Analysis Intelligence Index and delivers approximately 1.3x to 6.2x the throughput per MW at that user output speed, depending on hardware.

10/13Tue
  1. 量子位71

    Anthropic researchers test autonomous AI replication across simulated Android devices

    Jay reports that Anthropic researchers William Zhou and Daniel Freeman tested GLM-5.2 in an isolated network of 8 GCP nodes running Android 15 redroid emulators. It autonomously deployed Llama3.1-8B and propagated to all 8 nodes in 4 of 5 trials, reaching 7 in the remaining trial. Separately, researchers manually distributed Gemma4-26B-A4B across 15 Android TV boxes at 0.39 tokens per second, while Qwen3.5-9B deployment success rose from 45% to 61%–84% across later generations inheriting debugging notes.

  2. 宝玉 · X76

    Anthropic tests AI self-replication and projects smaller models could catch up this year

    Baoyu summarizes Anthropic research in which GLM-5.2 alone completed a replication workflow across simulated Android servers, while smaller models failed. The experiment used unauthenticated ADB access and ran GLM-5.2 remotely to deploy Llama3.1-8B, limiting what the results establish about autonomous spread. Anthropic roughly projects that smaller models could match GLM-5.2’s tested capability by year-end based on general capability trends. Researchers also manually distributed Gemma4-26B-A4B across 15 Android TV boxes, achieving 0.39 tokens per second.

    Why it matters: The experimental setup helps distinguish successful replication under simplified conditions from autonomous spread on consumer devices, clarifying the limits of the projected timeline for smaller models.

  3. Artificial Analysis · X70

    Artificial Analysis launches FrontierCode 1.1 leaderboard measuring maintainer-ready coding changes

    Artificial Analysis launched its FrontierCode 1.1 leaderboard, evaluating models on Cognition’s private coding benchmark using the 100-task Main set and its Stirrup open source reference harness. Grading combines unit tests with maintainer-oriented criteria; failing any blocking check yields zero. Claude Opus 5.5 (max with fallback) leads at 52%, with blockers passed in 58% of attempts; MiMo-V2.6-Pro leads open weights models at 45%, while GPT-6.1 Sol (max) scores 49% at $0.81 per task, matching Claude Fable 5.1 (max with fallback).

10/9Fri
  1. 量子位62

    ByteDance Seed finds why DeepSeek-V4 can be inconsistent: chunked KV-cache compression creates position sensitivity

    The ByteDance Seed team found that the DeepSeek-V4 series shows periodic fluctuations in long-context retrieval depending on where information appears, with a cycle of every 4 tokens. In the 128K needle-in-a-haystack test, moving the same information to a different position changed accuracy by as much as 40.2 percentage points.

10/4Sun
9/30Wed