AI Video Papers2026
Haoyu Zhao, Zihao Zhao +11Affiliation: National University of Singapore, Affiliation: Fudan University +1
Evaluates whether MiniMax-H3 can infer latent physical events from incomplete multimodal evidence and produce videos consistent with those inferences. Builds a 517-instance benchmark across four reasoning scenarios (multi-view, audio disambiguation, video continuation, audiovisual integration) and quantifies modality-specific failure modes.
AI Dataset2026
Benchmark for typed probabilistic decisions: given one shared state, a model answers multiple typed questions at once and returns full probability distributions (noul/choice/score). Contains four workflows with train/test splits and soft gold labels from teacher samples, designed to evaluate accuracy, calibration, and latency trade-offs.
Introduces physical rank consistency (PRC) and ActionPiece, a learned action tokenizer that preserves local physical relationships via joint supervision of representation learning and quantization, improving autoregressive vision-language-action policy performance across multiple benchmarks.
AI Agent Papers2026
Extracts replayable behaviors from working web apps and converts them into verifiable, reference-guided software-engineering tasks to evaluate and diagnose coding agents; includes an automated pipeline that scales to thousands of tasks.
AI Agent Papers2026
Finds token- and API-cost-saving harness mechanisms for long-horizon coding agents using automated recursive self-improvement; packages four surviving mechanisms (action fusion, context compaction, observation archiving, delegated reading) to cut recorded token traffic ~44.7–49.0% and API cost by about one third while preserving most capability.
AI Agent Papers2026
Measures how individual harness components—planning, action space, and context management—affect coding agents' success, cost, and behavior. Uses a modular harness across 176 matched settings on SWE-Bench Verified and Terminal-Bench 2.1 to isolate component effects and surface model- and budget-dependent trade-offs.
AI Agent Papers2026
Provides reproducible, multi‑platform environments and execution‑grounded verification for hybrid computer‑use agents that interleave GUI exploration and code/CLI. Key features: a five‑platform harness, RecreationBench (250 tasks), reference‑grounded programmatic and visual assertions for automatic scoring.
AI Dataset2026
Provides a complete benchmark and training release for native audio‑visual dialogue: 2,800 synthesized audio‑visual dialogues, tiered rubrics, scoring code, ~28GB of 1080p media, and an RL reward recipe to evaluate and train omni models that take video+audio and return text.
AI Video Papers2026
Zhenchen Tang, Yang Li +7New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Sciences +2
Introduces a rubric-based approach for video reward modeling that generates explicit, query-adaptive evaluation criteria before scoring to reduce scalar drift; proposes RGPO, a two-stage training (seed warm-up + joint optimization) that achieves strong pointwise and pairwise evaluation with high data efficiency.
Embodied AI2026
Uses a compact discrete-action interface (translation, rotation, gripper) to let frozen vision–language models control robots in closed-loop, and leverages in-context (one-shot) demonstrations to substantially improve manipulation success without robot-specific training.
Provides an open family of K–12 foundation models and a capability-oriented instruction-tuning corpus that teaches models to solve curriculum problems, map items to curriculum concepts, diagnose learner errors, and produce scaffolded pedagogical responses.
AI Dataset2026
Maps a state and question to typed probabilistic decisions (choice distributions, yes/no probabilities, or scored/ordinal outputs) across controlled synthetic tasks. Offers multiple frozen configs with train/calibration/validation/test/OOD splits, Parquet + raw JSONL exports, and reproducible manifests and provenance.