#paper

20 articles

01 ·Time Series·★ MEMBER·PAPER·8 min read Paper Walkthrough: PointWAM — Forecasting the Scene and the Hands as 3D Point Trajectories to Teach Robot Hands from Human Video PointWAM splits the world into scene points and hand keypoints, forecasts where both will move in one shared 3D frame, then converts the hand forecast into robot actions. Pre-trained on 1.15M human-video episodes, it lifts average DexJoCo success from 12.1% to 69.0%. A from-scratch walkthrough grounded in the paper. 02 ·RAG & Retrieval·★ MEMBER·PAPER·8 min read Paper Explained: Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation — Borrow Someone Else's Skill, Rewrite It for Your Own Harness RASO retrieves agent skills from a large public corpus and rewrites them in the vocabulary of the target execution harness before using them. With a rollout-free initialization stage and a failure-driven update stage, it beat retrieval-free optimizers across four benchmarks and two models. A from-scratch walkthrough of the paper. 03 ·★ MEMBER·PAPER·7 min read Paper Explained: Realtime-Venus — A Voice-and-Video Assistant That Keeps Talking While Work Runs in the Background Listening, speaking, and background delegation all share one clock ticking once per second. We walk through the 9B models and the harness that let an assistant hand off tool calls without pausing the conversation, from analogy to equations to pseudocode. 04 ·★ MEMBER·PAPER·10 min read Paper Walkthrough: MaP-WAM — Turning a Robot's Memory into Plans So the Executor Stays the Same Size as History Grows For robot tasks you can't solve without remembering the past, MaP-WAM stops feeding the executor the full video history and instead compresses it into a plan for what to do next. The executor predicts its own progress alongside actions to decide when a step is done. We walk through the design, which reports 83.3% on RMBench and 78.0% on real robots, straight from the paper. 05 ·★ MEMBER·PAPER·8 min read Paper Explained: LIT — Stopping Robots from Cheating on Visual Cues by Giving Vision a Single Doorway Robot foundation models learn to lean on irrelevant cues like backgrounds and lighting. LIT first teaches goal-directed motion without images, then lets vision reach the action expert only through pose-predicting latent tokens — improving LIBERO-Plus success on all four architectures tested. 06 ·Inference & Serving·★ MEMBER·PAPER·7 min read Paper Explained: Steering Geometry — Do LLM Steering Vectors Trace the Map of Human Values? Activation steering nudges an LLM's hidden states at inference time to change its behavior. This paper asks whether those nudges follow the circular structure of human values, using a 20-value, ~26K-sample benchmark. Explained from zero. 07 ·How Transformers Work·★ MEMBER·PAPER·7 min read Paper Explained: LimiX-2 — A Tabular Foundation Model That Learns the Whole Table, Not Just the Answer Column LimiX-2 from the ground up: the shift from PFNs that predict a single target to Contextual Mechanism Networks that learn a table's joint distribution, cell-level representations, pretraining on synthetic causal data, and what the three benchmarks do and don't show. 08 ·★ MEMBER·PAPER·12 min read Paper walkthrough: Qwen-Drive-1.0 — bolting 3D perception and planning onto a VLM without touching its architecture Leave the pretrained VLM exactly as it is, attach a BEV perception head and a Planning Expert, and you get 3D detection, occupancy, maps and trajectory generation in one model — general vision-language ability nearly intact, 90.7 PDMS on NAVSIM. A ground-up walkthrough, straight from the paper. 09 ·FREE·PAPER·10 min read Paper Walkthrough: GigaBrain-0.7 — 37,000 Hours of Embodied Experience and a Three-System Robot Brain A ground-up walkthrough of GigaBrain-0.7, which splits a robot foundation model into understanding, prediction/evaluation, and action, then pretrains it on 37,256.98 hours of embodied data in a single stage. Covers subgoal-image and progress-value conditioning, Soft Knowledge Insulation, and the real-robot numbers — strictly from the paper. 10 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: Embodied-Navigator (TAMP-Nav) — Let the VLM Just Point, and Navigation Gets Both Faster and Better Instead of asking a VLM for 3D coordinates, have it point at a 2D pixel; think and store memory only at key nodes; align it all with two-level GRPO. A ground-up walkthrough of the design that hits 66.2% SR on R2R-CE at 16.58s per task using only 90k training trajectories. 11 ·Paper Deep-Dives·★ MEMBER·PAPER·11 min read Paper walkthrough: Apodex 1.1 — scaling agents around completed work Not a bigger model and not more thinking time — Apodex 1.1 scales two other surfaces: the environments an agent learns in, and the way work is organised across agents. A walkthrough from the task contract to the AgentOS delivery gate, the numbers, and the limits. 12 ·Agents·★ MEMBER·PAPER·13 min read Paper walkthrough: Zetta ζ — a robot harness that repairs itself mid-execution, with the policy frozen Existing embodied agents only reflect once an episode is over. Zetta instead runs code-based critics at action frequency and intervenes the moment a failure signature appears — without touching a single policy weight. A ground-up walkthrough of the design, following the paper itself. 13 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough — FACET: Grounding Instruction, Environment, Solution and Verifier in One Executable State A terminal task is a four-part bundle: instruction, environment, reference solution, verifier. FACET builds and runs the environment first, then uses that realized state as shared ground for every other artifact. From 71K agent skills it produces 6,078 validated tasks, and 1.2K successful trajectories lift Qwen3.5 at 4B, 9B and 27B alike. 14 ·Inference & Serving·★ MEMBER·PAPER·15 min read Paper Explained: Agentic ESOpt — Drop Backprop, Jiggle the Weights, and Train Long-Horizon LLM Agents No gradients at all: spawn G perturbed copies of the model, run them in the environment, and nudge the weights toward the ones that scored well. That buys full-parameter updates of a 27B agent at inference-level memory (8.41GB) — and a 12.50-point lead over GRPO on 15-move Sudoku. A ground-up walkthrough of the NUS-led paper. 15 ·CNNs & Image Recognition·★ MEMBER·PAPER·8 min read Paper Walkthrough: TurboVLA — Kick the LLM Out of the Loop and Run a Robot Policy at 32 Hz on an RTX 4090 with Under 1 GB of VRAM Mainstream VLA models route every control step through a large language model. TurboVLA removes the LLM and fuses vision and instructions with lightweight bidirectional cross-attention instead — hitting 97.7% on LIBERO with 0.2B parameters, 31.2 ms latency, and 0.9 GB of VRAM. A from-scratch walkthrough of the paper. 16 ·Agents·★ MEMBER·PAPER·13 min read Paper Deep-Dive: Recursive Synthesis — Extending Verified Tasks Into 40,000 Long-Horizon Terminal Problems Long-horizon terminal tasks cost hundreds to thousands of dollars each to author. RST mass-produces them for about $0.05 by recursively extending already-verified seeds. Fifteen rounds yielded 37,484 tasks, with oracle solutions growing from a median of 67 lines to 374, and the resulting trajectories genuinely improved Qwen3.5 under SFT and PPO — a walkthrough of the data-synthesis paper from Tencent and collaborators. 17 ·Agents·FREE·PAPER·10 min read Paper Explained: OSReward — Can You Trust the AI That Grades AI? Remeasuring Rewards for Computer-Use Agents The VLM judges that decide whether an agent driving a PC or a phone succeeded turn out to share a leniency bias that reads failure as success — a paper that exposes it with 1,019 human-verified trajectories and closes the gap with OS-Shepherd, an open reward model 30–60× cheaper, explained from zero background. 18 ·Agents·FREE·PAPER·8 min read Paper Walkthrough: Metis — A 'Memory Foundation Model' That Moves Agent Memory Inside the Model Agent memory today is mostly bolted on from the outside via RAG. This paper proposes memory foundation models — models whose forward pass natively stores, forgets, and updates information — and builds Metis, the first prototype. A from-scratch walkthrough of how it works, how well it works, and where it breaks. 19 ·Agents·FREE·PAPER·8 min read Paper Explained: LongHorizon-Harness — Long-Horizon Agent Tasks Are a State-Management Problem, Not an Execution Problem Without touching the model at all, keeping task state outside execution and running a Manage-Execute-Audit loop dramatically improves long-horizon task performance — a walkthrough of Alibaba DreamX team's harness-design paper, from analogy to pseudocode. 20 ·Paper Deep-Dives·★ MEMBER·PAPER·8 min read Paper Deep Dive: AskChem — Changing the Unit of Search from Papers to Provenance-Carrying Claims An NYU-led systems paper that swaps the retrieval unit of chemistry literature search from papers to claims grounded by a DOI and a verbatim quote. We walk through the 2.4M-claim index, evidence graph, and hybrid search design, the evaluation that took DOI resolution from 88.3% to 100%, and the paper's honest limitations.