Only meaningful ML signals. Papers, repos & datasets. Selected, not collected. Home of The Weekly ML Signal → https://teapaw.org/t.co/wUFGgFytuv
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Too much ML to keep up with? We pick what's worth a look.
Daily picks of ML papers, repos and datasets, here on X.
The week's best by email every Monday: The Weekly ML Signal → papersdatacode.com/?utm_sour…
The full feed on Telegram → t.me/papersdatacode
Every pick, archived → papersdatacode.com/archive?u…
Selected, not collected.
Weekly Top · Nov 02 – Nov 08
Follow for next week’s top
📄 Paper: Proving and Improving the Platonic Representation Hypothesis with JEPAs
It proves cross-modal JEPA encoders align up to rotation and beats 72B LMs with 42x fewer samples.
💻 Repo: hyz317/HairDeck
Unlike most single-image 3D tools, it outputs hair cards, spring-bone rigs, and GLB/VRM exports together.
📊 Dataset: Axion1-0/axion-training-data
Unlike mostly evaluation benchmarks, it releases 18,363 full agent-training trajectories plus 484 RL tasks with verified tool-use.
Full issue: papersdatacode.com/issues/12…
ALT ML Weekly Top, Nov 02 – Nov 08: Real-world agent evaluation. Long-horizon benchmarks, tool-use trajectories, and robot action labels.
📋 ML Weekly Recap · Nov 02 – Nov 08
⚡ Trends
▸ Benchmarks increasingly target long-horizon, real-world agent and robotics evaluation
▸ Unified multimodal models replace modality-specific stacks with shared representations or schemas
▸ Inference efficiency advances emphasize streaming, speculative decoding, and adaptive runtime routing
🧭 TL;DR
📄 Proving and Improving the Platonic Representation Hypothesis with JEPAs
Randall Balestriero, Yann LeCun
Proves cross-modal alignment and beats giant LMs with 42x fewer samples
📄 arxiv.org/abs/2610.39302
⭐ Mika
Practical on-device Android automation across apps without APIs or cloud
⭐ github.com/wangzan101/Mika
💡 Agents are moving from toy tasks toward real-world, verifiable workflows.
→ t.me/papersdatacode_digests
ALT ML Weekly Recap · Nov 02 – Nov 08. Long-horizon agent benchmarks; Unified multimodal models; Inference efficiency.
💻 GenLang-V/GenDB-V
Builds verified Rust query engines for a schema or hot query template by translating SQL to a fixed spec, then generating code and proofs checked by Verus.
Key points:
• Up to 61.3× over DuckDB and 28.3× over Umbra on SEC-EDGAR
• 51.6× over DuckDB and 22.0× over Umbra on TPC-H 22 queries
• Verus proves correctness, memory safety, arithmetic safety, no data races
Shows AI can synthesize database engines that are both formally verified and substantially faster than mature systems on targeted workloads.
⭐ 15 stars
ALT Pipeline diagram showing GenDB-V synthesis, verification, and trust boundaries
📄 ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents
by Guanting Dong (@kakakbibibi), Zhichao Sun et al.
A trace-first framework that replaces data-centric environment synthesis with execution-witnessed tool graphs and replay-verified task generation across environments.
Key points:
• Qwen3.6-35B-A3B improves from 23.0 to 41.0 avg; 43.2 with RL
• GLM-5.2 improves from 44.6 to 56.6 avg across 7 benchmarks
• On Pro benchmarks, 7 frontier models drop by up to 38.2 points
Real execution-witnessed tool composition is a stronger primitive for training and benchmarking long-horizon agents than synthetic, LLM-judged environment graphs.
ALT Two-stage pipeline diagram showing the full method for task synthesis
💻 inclusionAI/IDM
Runs 1-step or 2-step text-to-image sampling from one checkpoint and improves text-heavy prompts using EMA feature-moment matching plus internal DMD.
Key points:
• 94.25% LongText-CN and 90.20% LongText-EN at 2 NFE
• 0.25s at 1 NFE and 0.30s at 2 NFE on one A100, BF16
• EMA moment loss adds +0.41 LongText-EN over internal DMD alone
Shows that a generator's own EMA features can replace an external encoder for moment matching in few-step text-to-image distillation.
⭐ 240 stars
ALT Method pipeline showing DMD loss and feature-moment loss architecture
📄 Route Anything: A Study of Model and Policy Routing for General-Purpose AI Systems
by Pengfei Zhou, Yixing Ma et al.
Per-input routing across heterogeneous models and robot policies, optimized for quality-cost tradeoffs and trained on candidate outcome prediction.
Key points:
• Oracle routing beats best fixed by 5.4 pts across 24 benchmarks
• Learned routers gain 2.1 pts on video generation vs best fixed
• Robot routing reaches 71.6% vs 70.5% best single policy with 24% less time
Shows that much of the gain in general-purpose AI systems comes from input-level orchestration of specialized candidates, not from picking one globally best model.
ALT Shows router pipeline plus headline routing gains and efficiency, core contribution
💻 wangzan101/Mika
by @wangzan101
Operates Android apps and websites from chat prompts, using a background screen so you can keep using the phone while it works.
Key points:
• No Mika server; screen data goes only to the configured model provider
• Works with any app via screen understanding, not per-app APIs
• Compatibility self-check runs 12 checks in about a minute
Shows that a practical cross-app Android agent can run locally on the phone while staying usable during execution.
⭐ 6.6k stars
ALT Screenshot showing AI agent operating phone app, demonstrating product capability
📄 Library of Layers (LoL): Adaptively Ordering Transformer Layers at Runtime
by Abdellah Rahmani (@arahmani_AR), Sean McLeish (@SeanMcleish) et al.
A router selects the next block from a shared layer library at each depth, so execution order changes per input instead of staying fixed.
Key points:
• Safety probe on sequence-level routing features reaches 0.95 AUROC
• Maze strategy control hits 99.79-100% correct forced-strategy traces
• Sudoku forced routing yields 99.5-100% requested-strategy accuracy
Shows that layer order can be a learned, controllable part of transformer computation rather than a fixed architectural choice.
ALT Detailed method diagram showing layer architecture, strided attention, and router mechanism
📄 CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements
by Xinlei Yu, Gen Li et al.
Open-world video workflows become seed tasks, then reusable primitives synthesize controllable GUI/CLI tasks with rollback-based data collection.
Key points:
• 80.2 avg on OSWorld Verified, competitive with top closed-source agents
• 43.9 avg on WeaveBench, above UI-Mate-27B's 18.3
• 25.0 full-pass on Agents' Last Exam, above Qwen-CUA's 5.3
Shows open-world seeded, recursively targeted data generation scales computer-use agents better than closed self-improvement alone.
ALT Detailed pipeline diagram showing the recursive flywheel method and components
📄 Wave Forcing: Towards Speed-of-Light Streaming Video Generation
by Yijie Jin, Yuxuan Liu et al.
Pipelines chunk denoising and history refresh across replicas, using causal access to cleaner in-flight chunks to sustain continuous video output.
Key points:
• 134.8 end-to-end FPS and 147.6 DiT FPS on 8 H200s with 4-step WF
• 5-step WF: 81.64 VBench Total vs 81.15 for Rolling Forcing
• Long-video Dynamic 91.45 vs 36.63 for Rolling Forcing; Subject 97.10 vs 92.75
Shows streaming video generation can reach near-real-time throughput without giving up long-horizon motion or subject consistency.
ALT Method diagram showing wavefront denoising, causal K/V delivery, and continuous output pipeline
📄 On the SPOT: Enhancing Speculative Decoding for Diffusion Language Models via Commit-Order Prediction
by Eliezer Shlomi, Eliron Rahimi et al.
A learned commit-order drafter for DLMs that reuses stale token predictions and verifies drafted state trees exactly with shared-noise sampling.
Key points:
• Up to 3.4x throughput vs standard decoding on SDAR-8B
• Uses 16-36% fewer target forwards than Spiffy on SDAR-8B
• SPOT-Sets adds 59% avg throughput over threshold decoding alone
For diffusion LM decoding, predicting where to commit next matters more than predicting the token itself.
ALT Method diagram comparing target decoding vs SPOT tree-based parallel decoding approach
