💻 Repo #Repo #Robotics #InertialOdometry #SelfSupervised

Kiss Imu
👤 sparolab

🎯 Task
Self-supervised inertial odometry

💡 Idea
Train an IMU odometry model from raw IMU plus LiDAR-odometry pseudo-labels, using motion-balanced sampling and a frequency gate to better cover under-represented motion regimes.

✨ Why it's interesting
Handles under-represented motion regimes during training via motion-balanced sampling and frequency gating.

💻 Repo
⭐ sparolab/KISS-IMU — 63 stars (+43 3d)
Python

🔗 paper

via @Papers.Data.Code

📄 Paper #Paper #LLM #TestTimeScaling #MultiAgentReasoning

TMAS: Scaling Test-Time Compute via Multi-Agent Synergy
👤 George Wu, Nan Jing, Qing Yi et al.

🎯 Task
Test-time scaling for LLM reasoning

💡 Idea
Multi-agent inference with hierarchical memories: an experience bank stores reliable intermediate conclusions and feedback, while a guideline bank tracks explored strategies to avoid redundancy; hybrid-reward RL trains correctness, memory use, and novel exploration.

✨ Why it's interesting
On challenging reasoning benchmarks, it shows stronger iterative scaling than prior TTS baselines.

💻 Repo
⭐ george-QF/TMAS-code — 4 stars

🔗 paper

via @Papers.Data.Code

📊 Dataset #Dataset #Multimodal #HyperspectralImaging #RemoteSensing

Hyperspectral Invasive Detection Dataset
👤 ziya07

🎯 Task
Hyperspectral invasive plant classification

💡 Idea
Hyperspectral vegetation observations with .mat image cubes plus tabular metadata: 10 spectral bands, PCA and Gabor features, geolocation, environmental variables, species/status labels, confidence, and ground truth across ecological regions.

✨ Why it's interesting
Combines spectra, texture, environment, and verified labels to support invasive-species detection and ecological mapping studies.

Downloads: 33 | Likes: 13

🔗 dataset

via @Papers.Data.Code

🔥 Repo #Repo #LLM #Metal #KvCache

Ds4
👤 antirez

🎯 Task
Local LLM inference and serving

💡 Idea
Run DeepSeek V4 Flash locally on Apple Metal with a model-specific engine, chat CLI, OpenAI/Anthropic-compatible server, long-context support, and disk-persistent KV cache to reuse prompt prefixes across sessions.

✨ Why it's interesting
Supports up to 1M-token context and disk KV persistence; reports 468 t/s prefill on M3 Ultra q2.

💻 Repo
⭐ antirez/ds4 — 8.0k stars (+5.3k 3d)
C


via @Papers.Data.Code

📄 Paper #Paper #LLM #MemoryMechanisms #Attention

δ-mem: Efficient Online Memory for Large Language Models
👤 Jingdi Lei, Di Zhang, Junxian Li et al.

🎯 Task
Long-term memory augmentation for LLMs

💡 Idea
Instead of storing history as extra tokens, retrieval text, or static adapters, it keeps a fixed-size online associative state and turns its readout into low-rank attention corrections for a frozen backbone.

✨ Why it's interesting
With only an 8×8 state, average score reaches 1.10× the frozen backbone and 1.15× the best non-δ-mem baseline; 1.31× on MemoryAgentBench and 1.20× on LoCoMo.

💻 Repo
⭐ declare-lab/delta-Mem — 53 stars

🔗 paper

via @Papers.Data.Code

📊 Dataset #Dataset #Tabular #Epidemiology #InfectiousDisease

🦠 Hantavirus (Andes Virus) — Global Epidemiology
👤 zkskhurram

🎯 Task
Infectious disease epidemiology analysis

💡 Idea
7 linked tables covering 25 countries across 5 WHO regions: yearly data from 1993–2025, outbreaks, monthly trends, clinical outcomes, environmental risk factors, virus strains, and a consolidated master table.

✨ Why it's interesting
Combines epidemiology, clinical, environmental, and strain data in one dataset, enabling cross-country HPS/HFRS trend and risk analysis from a single source.

Size: 7 tables, 25 countries, 1993–2025

📊 Dataset
📥 662 downloads
❤️ 26 likes

🔗 dataset

via @Papers.Data.Code

💻 Repo #Repo #CV #4dReconstruction #DynamicScenes

D4rt
👤 lucidrains

🎯 Task
dynamic scene reconstruction from video

💡 Idea
Predict 3D points in dynamic scenes from video plus coordinate and time queries, with a trainable PyTorch model that can return losses for supervision or direct point predictions.

✨ Why it's interesting
Provides a ready-to-use D4RT implementation with batched variable-length video/query handling for 4D reconstruction experiments.

💻 Repo
⭐ lucidrains/d4rt — 50 stars (+50 3d)
Python


via @Papers.Data.Code

📄 Paper #Paper #Multimodal #VisionLanguageModels #ImageGeneration

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
👤 Haiwen Diao, Penghao Wu, Hanming Deng et al.

🎯 Task
Unified multimodal understanding and generation

💡 Idea
Instead of bolting together encoder-based understanding and VAE/diffusion generation, it uses one native pixel-text backbone with shared attention and stream-specific MoT blocks, trained jointly for text prediction and pixel-space flow matching.

✨ Why it's interesting
Authors claim it rivals top understanding-only VLMs and outperforms prior open-source unified models across understanding, reasoning, and generation; generation runs at 32× compression.

💻 Repo
⭐ OpenSenseNova/SenseNova-U1 — 1.7k stars

🔗 paper

via @Papers.Data.Code

📄 Paper #Paper #CV #VideoGeneration #DiffusionModels

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
👤 Yuchao Gu, Guian Fang, Yuxin Jiang et al.

🎯 Task
Any-step video generation

💡 Idea
Instead of endpoint consistency maps for fixed few-step sampling, it learns arbitrary-time flow-map transitions along the full ODE path, then uses shortcut backward simulation for on-policy distillation to cut discretization error and causal exposure bias.

✨ Why it's interesting
On 14B T2V, it gets 84.05 VBench at 4 NFEs and 84.41 at 32; beats Krea-Realtime-14B's 83.25 at 4 and rCM-14B's 83.73 at 4.

💻 Repo
⭐ NVlabs/AnyFlow — 202 stars
⭐ NVLabs/AnyFlow — 202 stars

🔗 paper

via @Papers.Data.Code

📄 Paper #Paper #LLM #ReinforcementLearning #AgentTraining

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
👤 Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang et al.

🎯 Task
Long-form deep research agent training

💡 Idea
Instead of using rubrics only to score final answers, RubricEM uses them to structure execution, reward each stage, and store experience. It decomposes research into Plan/Research/Review/Answer, applies stagewise GRPO for denser credit, and jointly trains a reflection policy as reusable memory.

✨ Why it's interesting
RubricEM-8B outperforms comparable open models on 4 long-form research benchmarks and approaches proprietary deep-research systems after 1400 RL steps.

🔗 paper

via @Papers.Data.Code

📊 Dataset #Dataset #NLP #StemReasoning #VisualQuestionAnswering

open-mm-rl
👤 TuringEnterprises

🎯 Task
Multimodal STEM question answering

💡 Idea
40 MIT-licensed STEM QA examples across physics, math, biology, and chemistry, spanning single-image, multi-panel, and multi-image formats with deterministic final answers.

✨ Why it's interesting
Deterministic, programmatically checkable answers make advanced multimodal STEM reasoning benchmarkable for RL and outcome-supervised training.

Size: 40 examples, 15.5 MB

📊 Dataset
📥 2.6k downloads
❤️ 94 likes

🔗 dataset

via @Papers.Data.Code

💻 Repo #Repo #CV #ImageToVideo #2kGeneration

Swifti2v
👤 HKUST-LongGroup

🎯 Task
high-resolution image-to-video generation

💡 Idea
Generate native 2K videos from a single image by first producing a low-res motion reference, then refining to high resolution while conditioning on both the input image and the Stage I video.

✨ Why it's interesting
Matches strong 2K end-to-end I2V baselines on key VBench-I2V metrics with 202× less GPU-time; 81-frame 2K output runs in ~111s on one H800 and fits on a 24 GB RTX 4090.

💻 Repo
⭐ HKUST-LongGroup/SwiftI2V — 71 stars (+47 3d)
HTML

🔗 paper

via @Papers.Data.Code

📋 Weekly Digest · May 09 – May 16
#WeeklyDigest

📄 Papers

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
#VideoGeneration #DiffusionModels #Distillation
Any-step video diffusion ⟶ 84.05 VBench at 4 NFEs
→ Learn more...

Flow-OPD: On-Policy Distillation for Flow Matching Models
#TextToImage #KnowledgeDistillation #ReinforcementLearning
On-policy flow distillation ⟶ boosts GenEval and OCR
→ Learn more...

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
#VisionLanguageModels #ImageGeneration #MixtureOfExperts
NEO-unify multimodal model ⟶ unifies understanding and generation
→ Learn more...

δ-mem: Efficient Online Memory for Large Language Models
#MemoryMechanisms #Attention #ParameterEfficientTuning
Online associative memory ⟶ steers attention for long-horizon tasks
→ Learn more...

LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
#TestTimeScaling #Reasoning #AgenticSearch
Offline replay controller ⟶ improves accuracy-cost tradeoffs
→ Learn more...

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
#ReinforcementLearning #AgentTraining #LongContextReasoning
Rubric-guided meta-RL ⟶ stagewise credit for research agents
→ Learn more...

💻 Repos

antirez/ds4 ⭐
#Metal #KvCache #OpenaiCompatible
Metal local inference ⟶ 1M context with disk KV cache
→ Learn more...

facebookresearch/ProgramBench ⭐
#Benchmark #SoftwareEngineering #ReverseEngineering
Program reconstruction benchmark ⟶ tests LM reverse engineering
→ Learn more...

sparolab/KISS-IMU ⭐
#InertialOdometry #SelfSupervised #LidarPseudoLabels
Self-supervised IMU odometry ⟶ denoises raw IMU with LiDAR labels
→ Learn more...

📊 Datasets

AI Index Data: Growth, Talent (Cambridge/Harvard)
#GlobalAI #CountryIndicators #LongitudinalData
Global AI panel dataset ⟶ cross-country trend forecasting
→ Learn more...

giant-permissive-image-corpus
#ImageGeneration #PermissiveLicense #ImageDataset
Permissive image corpus ⟶ trains visual generation
→ Learn more...

➡️ Tomorrow — Efficient ML Monthly

via @Papers.Data.Code

📈 Monthly · Efficient ML · Apr 17 – May 17
#MonthlyDigest #EfficientML

📄 Papers

Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
#DiffusionModels #DecisionTrees #KnowledgeDistillation
Trees and flows ⟶ faster tabular generation
→ Learn more...

📊 Datasets

MSR-ACC/TAE25
#QuantumChemistry #AtomizationEnergy #CoupledCluster
Quantum chemistry dataset ⟶ trains atomization energy models
→ Learn more...

AI Index Data: Growth, Talent (Cambridge/Harvard)
#GlobalAI #CountryIndicators #LongitudinalData
Global AI panel dataset ⟶ cross-country trend forecasting
→ Learn more...

WHO Global Health Indicators for Prediction
#GlobalHealth #CountryLevel #WorldBank
Global health panel data ⟶ cross-country trend analysis
→ Learn more...

⚡ Trends

▸ Longitudinal country-level datasets increasingly target forecasting and cross-country trend analysis.
▸ Wide, linked, multi-table dataset formats are becoming standard for benchmarking.
▸ Efficiency gains come from unifying model families and distilling complex systems.

🧭 TL;DR

📄 Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
Unifies trees and diffusion, delivering faster tabular generation and effective distillation.

💡 Efficiency advances increasingly come from unifying classical structures with generative modeling.

via @Papers.Data.Code

📄 Paper #Paper #LLM #Reasoning #ReinforcementLearning

Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
👤 Yafu Li, Runzhe Zhan, Haoran Zhang et al.

🎯 Task
Olympiad-level mathematical and scientific reasoning

💡 Idea
Instead of domain-specific systems, it uses one scaling recipe: reverse-perplexity long-CoT SFT to instill proof search and self-checking, then coarse verifiable-reward RL, proof-level RL with self-refinement/replay, and test-time verification loops.

✨ Why it's interesting
SU-01 gets 57.6% on IMO-ProofBench, 70.2% with test-time scaling, and reaches the IMO 2025 gold line with 35 points.

💻 Repo
⭐ Simplified-Reasoning/SU-01 — 68 stars

🔗 paper

via @Papers.Data.Code

📊 Dataset #Dataset #LLM #SoftwareEngineering #ToolUse

Orchard
👤 microsoft

🎯 Task
Agentic software engineering and web GUI interaction

💡 Idea
~110K agent trajectories in 2 parallel subsets: 107,185 SWE chat+tool rollouts over 2,788 GitHub repos with hidden-test pass/fail labels, plus 3,070 GUI decision-point rows with screenshots, chat context, and judge-verified rewards across 409 web tasks.

✨ Why it's interesting
Verified patch outcomes and judge-scored GUI steps make agent training and evaluation measurable across real coding and browser tasks.

Ⓢ 110,255 samples, ~10.97 GB

🔗 dataset

via @Papers.Data.Code

💻 Repo #Repo #CV #DepthEstimation #CameraPose

vggt Omega
👤 facebookresearch

🎯 Task
multi-view camera and depth reconstruction

💡 Idea
Infer camera parameters and per-image depth from a set of images in one forward pass, and optionally produce text-aligned embeddings for the same visual inputs.

✨ Why it's interesting
Runs end-to-end on a single A100 with 6.02 GB for 1 frame and 43.15 GB for 500 frames, with released 1B checkpoints and demo code.

💻 Repo
⭐ facebookresearch/vggt-omega — 413 stars (+413 3d)
Python


via @Papers.Data.Code

📄 Paper #Paper #CV #VideoGeneration #DiffusionModels

Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation
👤 Min Zhao, Hongzhou Zhu, Kaiwen Zheng et al.

🎯 Task
Real-time autoregressive video generation

💡 Idea
Instead of costly AR-teacher ODE trajectory distillation, it initializes few-step AR students with causal consistency distillation: same AR flow-map target, but learned from single online adjacent-step teacher updates, making frame-wise 1-2 step rollout practical.

✨ Why it's interesting
At frame-wise 2-step, beats 4-step chunk-wise Causal Forcing by +0.1 VBench Total, +0.3 Quality, +0.335 VisionReward; 50% lower first-frame latency, ~4x cheaper Stage 2.

💻 Repo
⭐ thu-ml/Causal-Forcing — 665 stars

🔗 paper

via @Papers.Data.Code

📄 Paper #Paper #Multimodal #LongContextModeling #VisionLanguageModels

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context
👤 Zhaowei Wang, Lishu Luo, Haodong Duan et al.

🎯 Task
Long-context vision-language modeling

💡 Idea
Instead of OCR-style long-data training, use instruction-formatted long-document VQA. A balanced length mix and retrieval-heavy task mixture beat 128K-focused or transcription-based training for extending LVLM context.

✨ Why it's interesting
With 5B tokens, MMProLong improves long-doc VQA by 7.1%, stays strong at 256K/512K beyond 128K training, and exceeds baselines by 20%+ there.

🔗 paper

via @Papers.Data.Code

📊 Dataset #Dataset #LLM #HallucinationDetection #Multilingual

LLM Hallucination Benchmark Dataset
👤 alitaqishah

🎯 Task
LLM hallucination detection and analysis

💡 Idea
200 annotated LLM responses spanning 5 models, 8 domains, 7 languages, 7 hallucination types, 4 annotator types, and 4 mitigation strategies, with prompt, response, hallucination label, span, severity, and verified correction.

✨ Why it's interesting
Makes cross-model, multilingual hallucination detection and mitigation evaluation directly measurable with typed error labels and corrected references.

Ⓢ 200 annotated responses

🔗 dataset

via @Papers.Data.Code

📄 Paper #Paper #LLM #ReinforcementLearning #KnowledgeDistillation

Self-Distilled Agentic Reinforcement Learning
👤 Zhengxi Lu, Zhiyuan Yao, Zhuowen Han et al.

🎯 Task
Agentic RL for multi-turn LLMs

💡 Idea
Instead of naively mixing OPSD with RL, SDAR keeps RL as the backbone and uses detached token-level gates to apply distillation selectively—amplifying positive teacher-student gaps and softening negative teacher rejections.

✨ Why it's interesting
Beats GRPO by +9.4% on ALFWorld, +7.0% on Search-QA, and +10.2% WebShop-Acc, while avoiding naive GRPO+OPSD instability.

💻 Repo
⭐ ZJU-REAL/SDAR — 96 stars

🔗 paper

via @Papers.Data.Code