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authoritative_research_report.json

@@ -1,6 +1,6 @@
 {
-  "generated_at": "2026-05-17 16:15:51 UTC",
-  "total_arxiv_papers": 0,
+  "generated_at": "2026-05-17 16:16:40 UTC",
+  "total_arxiv_papers": 10,
   "total_hf_papers": 15,
   "total_github_repos": 10,
   "hf_papers": [
@@ -110,7 +110,163 @@
       "summary": "On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own rollouts while conditioning on the reference solution. A design choice shared by nearly all such methods, however, has gone unquestioned: the teacher always sees the full reference reasoning. We argue that this default itself is part of the problem and identify a teacher-side exposure mismatch: when the teacher conditions on reasoning far beyond the student's current competence, the resulting token targets become too strong to absorb. A controlled fixed-exposure sweep makes this concrete on two fronts: 1) full exposure is not reliably the best choice, and 2) student-teacher mismatch grows monotonically as the teacher sees more privileged reasoning. This motivates treating teacher exposure not as a fixed hyperparameter but as a learnable training-time control variable. We therefore propose Adaptive Teacher Exposure for Self-Distillation (ATESD). ATESD models the reveal ratio with a lightweight Beta-policy controller conditioned on compact training-state statistics, and uses one sampled exposure for a short hold window of student updates. To make this exposure controller learnable, we optimize it with a discounted learning-progress reward that scores each held decision by its effect on the student's future improvement rather than its immediate loss change, addressing the delayed credit assignment induced by on-policy distillation. Experiments on AIME 24, AIME 25, and HMMT 25 across Qwen3-{1.7B, 4B, 8B} show that ATESD consistently outperforms competitive self-distillation and RL baselines, improving over OPSD by +0.95, +2.05, and +2.33 Average@12 points respectively, and establishing adaptive teacher exposure as an effective new axis for reasoning self-distillation."
     }
   ],
-  "arxiv_papers": [],
+  "arxiv_papers": [
+    {
+      "id": "2605.15199v1",
+      "title": "EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation",
+      "url": "http://arxiv.org/abs/2605.15199v1",
+      "summary": "Multi-shot video generation extends single-shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and locations across shots remains a challenge over long sequences. Existing evaluations typically use independently generated prompt sets with limited entity coverage and simple consistency metrics, making standardized comparison difficult. We introduce EntityBench, a benchmark of 140 episodes (2,491 shots) derived from real narrative media, with explicit per-shot entity schedules tracking characters, objects, and locations simultaneously across easy / medium / hard tiers of up to 50 shots, 13 cross-shot characters, 8 cross-shot locations, 22 cross-shot objects, and recurrence gaps spanning up to 48 shots. It is paired with a three-pillar evaluation suite that disentangles intra-shot quality, prompt-following alignment, and cross-shot consistency, with a fidelity gate that admits only accurate entity appearances into cross-shot scoring. As a baseline, we propose EntityMem, a memory-augmented generation system that stores verified per-entity visual references in a persistent memory bank before generation begins. Experiments show that cross-shot entity consistency degrades sharply with recurrence distance in existing methods, and that explicit per-entity memory yields the highest character fidelity (Cohen's d = +2.33) and presence among methods evaluated. Code and data are available at https://github.com/Catherine-R-He/EntityBench/.",
+      "authors": [
+        "Ruozhen He",
+        "Meng Wei",
+        "Ziyan Yang",
+        "Vicente Ordonez"
+      ],
+      "published": "2026-05-14T17:59:55+00:00"
+    },
+    {
+      "id": "2605.15198v1",
+      "title": "ATLAS: Agentic or Latent Visual Reasoning? One Word is Enough for Both",
+      "url": "http://arxiv.org/abs/2605.15198v1",
+      "summary": "Visual reasoning, often interleaved with intermediate visual states, has emerged as a promising direction in the field. A straightforward approach is to directly generate images via unified models during reasoning, but this is computationally expensive and architecturally non-trivial. Recent alternatives include agentic reasoning through code or tool calls, and latent reasoning with learnable hidden embeddings. However, agentic methods incur context-switching latency from external execution, while latent methods lack task generalization and are difficult to train with autoregressive parallelization. To combine their strengths while mitigating their limitations, we propose ATLAS, a framework in which a single discrete 'word', termed as a functional token, serves both as an agentic operation and a latent visual reasoning unit. Each functional token is associated with an internalized visual operation, yet requires no visual supervision and remains a standard token in the tokenizer vocabulary, which can be generated via next-token prediction. This design avoids verbose intermediate visual content generation, while preserving compatibility with the vanilla scalable SFT and RL training, without architectural or methodological modifications. To further address the sparsity of functional tokens during RL, we introduce Latent-Anchored GRPO (LA-GRPO), which stabilizes the training by anchoring functional tokens with a statically weighted auxiliary objective, providing stronger gradient updates. Extensive experiments and analyses demonstrate that ATLAS achieves superior performance on challenging benchmarks while maintaining clear interpretability. We hope ATLAS offers a new paradigm inspiring future visual reasoning research.",
+      "authors": [
+        "Ziyu Guo",
+        "Rain Liu",
+        "Xinyan Chen",
+        "Pheng-Ann Heng"
+      ],
+      "published": "2026-05-14T17:59:55+00:00"
+    },
+    {
+      "id": "2605.15196v1",
+      "title": "RefDecoder: Enhancing Visual Generation with Conditional Video Decoding",
+      "url": "http://arxiv.org/abs/2605.15196v1",
+      "summary": "Video generation powers a vast array of downstream applications. However, while the de facto standard, i.e., latent diffusion models, typically employ heavily conditioned denoising networks, their decoders often remain unconditional. We observe that this architectural asymmetry leads to significant loss of detail and inconsistency relative to the input image. To address this, we argue that the decoder requires equal conditioning to preserve structural integrity. We introduce RefDecoder, a reference-conditioned video VAE decoder by injecting high-fidelity reference image signal directly into the decoding process via reference attention. Specifically, a lightweight image encoder maps the reference frame into the detail-rich high-dimensional tokens, which are co-processed with the denoised video latent tokens at each decoder up-sampling stage. We demonstrate consistent improvements across several distinct decoder backbones (e.g., Wan 2.1 and VideoVAE+), achieving up to +2.1dB PSNR over the unconditional baselines on the Inter4K, WebVid, and Large Motion reconstruction benchmarks. Notably, RefDecoder can be directly swapped into existing video generation systems without additional fine-tuning, and we report across-the-board improvements in subject consistency, background consistency, and overall quality scores on the VBench I2V benchmark. Beyond I2V, RefDecoder generalizes well to a wide range of visual generation tasks such as style transfer and video editing refinement.",
+      "authors": [
+        "Xiang Fan",
+        "Yuheng Wang",
+        "Bohan Fang",
+        "Zhongzheng Ren",
+        "Ranjay Krishna"
+      ],
+      "published": "2026-05-14T17:59:52+00:00"
+    },
+    {
+      "id": "2605.15195v1",
+      "title": "VGGT-$Ω$",
+      "url": "http://arxiv.org/abs/2605.15195v1",
+      "summary": "Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also providing geometry-aware features useful for other tasks. Here, we show that the quality of these models scales predictably with model and data size. We do so by introducing VGGT-$Ω$, which substantially improves reconstruction accuracy, efficiency, and capabilities for both static and dynamic scenes. To enable training this model at an unprecedented scale, we introduce architectural changes that improve training efficiency, a high-quality data annotation pipeline that supports dynamic scenes, and a self-supervised learning protocol. We simplify VGGT's architecture by using a single dense prediction head with multi-task supervision and removing the expensive high-resolution convolutional layers. We also use registers to aggregate scene information into a compact representation and introduce register attention, which restricts inter-frame information exchange to these registers, in part replacing global attention. In this way, during training, VGGT-$Ω$ uses only about 30% of the GPU memory of its predecessor, allowing us to train with 15x more supervised data than prior work and to leverage vast amounts of unlabeled video data. VGGT-$Ω$ achieves strong results for reconstruction of static and dynamic scenes across multiple benchmarks, for example, improving over the previous best camera estimation accuracy on Sintel by 77%. We also show that the learned registers can improve vision-language-action models and support alignment with language, suggesting that reconstruction can be a powerful and scalable proxy task for spatial understanding. Project Page: http://vggt-omega.github.io/",
+      "authors": [
+        "Jianyuan Wang",
+        "Minghao Chen",
+        "Shangzhan Zhang",
+        "Nikita Karaev",
+        "Johannes Schönberger",
+        "Patrick Labatut",
+        "Piotr Bojanowski",
+        "David Novotny",
+        "Andrea Vedaldi",
+        "Christian Rupprecht"
+      ],
+      "published": "2026-05-14T17:59:51+00:00"
+    },
+    {
+      "id": "2605.15193v1",
+      "title": "Aligning Latent Geometry for Spherical Flow Matching in Image Generation",
+      "url": "http://arxiv.org/abs/2605.15193v1",
+      "summary": "Latent flow matching for image generation usually transports Gaussian noise to variational autoencoder latents along linear paths. Both endpoints, however, concentrate in thin spherical shells, and a Euclidean chord leaves those shells even when preprocessing aligns their radii. By decomposing each latent token into radial and angular components, we show through component-swap probes that decoded perceptual and semantic content is carried predominantly by direction, with radius contributing much less. We therefore project data latents onto a fixed token radius, use the radial projection of Gaussian noise as the spherical prior, finetune the decoder with the encoder frozen, and replace linear interpolation with spherical linear interpolation. The resulting geodesic paths stay on the sphere at every timestep, and their velocity targets are purely angular by construction. Under matched training, the method consistently improves class-conditional ImageNet-256 FID across different image tokenizers, leaves the diffusion architecture unchanged, and requires no auxiliary encoder or representation-alignment objective.",
+      "authors": [
+        "Tuna Han Salih Meral",
+        "Kaan Oktay",
+        "Hidir Yesiltepe",
+        "Adil Kaan Akan",
+        "Pinar Yanardag"
+      ],
+      "published": "2026-05-14T17:59:37+00:00"
+    },
+    {
+      "id": "2605.15190v1",
+      "title": "RAVEN: Real-time Autoregressive Video Extrapolation with Consistency-model GRPO",
+      "url": "http://arxiv.org/abs/2605.15190v1",
+      "summary": "Causal autoregressive video diffusion models support real-time streaming generation by extrapolating future chunks from previously generated content. Distilling such generators from high-fidelity bidirectional teachers yields competitive few-step models, yet a persistent gap between the history distributions encountered during training and those arising at inference constrains generation quality over long horizons. We introduce the Real-time Autoregressive Video Extrapolation Network (RAVEN), a training-time test framework that repacks each self rollout into an interleaved sequence of clean historical endpoints and noisy denoising states. This formulation aligns training attention with inference-time extrapolation and allows downstream chunk losses to supervise the history representations on which future predictions depend. We further propose Consistency-model Group Relative Policy Optimization (CM-GRPO), which reformulates a consistency sampling step as a conditional Gaussian transition and applies online Reinforcement Learning (RL) directly to this kernel, avoiding the Euler-Maruyama auxiliary process adopted in prior flow-model RL formulations. Experiments demonstrate that RAVEN surpasses recent causal video distillation baselines across quality, semantic, and dynamic degree evaluations, and that CM-GRPO provides further gains when combined with RAVEN.",
+      "authors": [
+        "Yanzuo Lu",
+        "Ronglai Zuo",
+        "Jiankang Deng"
+      ],
+      "published": "2026-05-14T17:59:30+00:00"
+    },
+    {
+      "id": "2605.15188v1",
+      "title": "FutureSim: Replaying World Events to Evaluate Adaptive Agents",
+      "url": "http://arxiv.org/abs/2605.15188v1",
+      "summary": "AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred. We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over the simulated period. We evaluate frontier agents in their native harness, testing their ability to predict world events over a three-month period from January to March 2026. FutureSim reveals a clear separation in their capabilities, with the best agent's accuracy being 25%, and many having worse Brier skill score than making no prediction at all. Through careful ablations, we show how FutureSim offers a realistic setting to study emerging research directions like long-horizon test-time adaptation, search, memory, and reasoning about uncertainty. Overall, we hope our benchmark design paves the way to measure AI progress on open-ended adaptation spanning long time-horizons in the real world.",
+      "authors": [
+        "Shashwat Goel",
+        "Nikhil Chandak",
+        "Arvindh Arun",
+        "Ameya Prabhu",
+        "Steffen Staab",
+        "Moritz Hardt",
+        "Maksym Andriushchenko",
+        "Jonas Geiping"
+      ],
+      "published": "2026-05-14T17:59:28+00:00"
+    },
+    {
+      "id": "2605.15187v1",
+      "title": "Articraft: An Agentic System for Scalable Articulated 3D Asset Generation",
+      "url": "http://arxiv.org/abs/2605.15187v1",
+      "summary": "A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close this gap and generate articulated assets at scale. We reduce the problem of generating an articulated 3D asset to that of writing a program that builds it. We then introduce a new agentic system, Articraft, that writes such programs automatically. We design a programmatic interface and harness to help the LLM do so effectively. The LLM writes code against a domain-specific SDK for defining parts, composing geometry, specifying joints, and writing tests to validate the resulting assets. The harness exposes a restricted workspace and interface to the LLM, validates the resulting assets, and returns structured feedback. In this way, the LLM is not distracted by details such as authoring a URDF file or managing a complex software environment. We show that this produces higher-quality assets than both state-of-the-art articulated-asset generators and general-purpose coding agents. Using Articraft, we build Articraft-10K, a curated dataset of over 10K articulated assets spanning 245 categories, and show its utility both for training models of articulated assets and in downstream applications such as robotics simulation and virtual reality.",
+      "authors": [
+        "Matt Zhou",
+        "Ruining Li",
+        "Xiaoyang Lyu",
+        "Zhaomou Song",
+        "Zhening Huang",
+        "Chuanxia Zheng",
+        "Christian Rupprecht",
+        "Andrea Vedaldi",
+        "Shangzhe Wu"
+      ],
+      "published": "2026-05-14T17:59:18+00:00"
+    },
+    {
+      "id": "2605.15186v1",
+      "title": "VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction",
+      "url": "http://arxiv.org/abs/2605.15186v1",
+      "summary": "High-quality 3D scene reconstruction has recently advanced toward generalizable feed-forward architectures, enabling the generation of complex environments in a single forward pass. However, despite their strong performance in static scene perception, these models remain limited in responding to dynamic human instructions, which restricts their use in interactive applications. Existing editing methods typically rely on a 2D-lifting strategy, where individual views are edited independently and then lifted back into 3D space. This indirect pipeline often leads to blurry textures and inconsistent geometry, as 2D editors lack the spatial awareness required to preserve structure across viewpoints. To address these limitations, we propose VGGT-Edit, a feed-forward framework for text-conditioned native 3D scene editing. VGGT-Edit introduces depth-synchronized text injection to align semantic guidance with the backbone's spatial poses, ensuring stable instruction grounding. This semantic signal is then processed by a residual transformation head, which directly predicts 3D geometric displacements to deform the scene while preserving background stability. To ensure high-fidelity results, we supervise the framework with a multi-term objective function that enforces geometric accuracy and cross-view consistency. We also construct the DeltaScene Dataset, a large-scale dataset generated through an automated pipeline with 3D agreement filtering to ensure ground-truth quality. Experiments show that VGGT-Edit substantially outperforms 2D-lifting baselines, producing sharper object details, stronger multi-view consistency, and near-instant inference speed.",
+      "authors": [
+        "Kaixin Zhu",
+        "Yiwen Tang",
+        "Yifan Yang",
+        "Renrui Zhang",
+        "Bohan Zeng",
+        "Ziyu Guo",
+        "Ruichuan An",
+        "Zhou Liu",
+        "Qizhi Chen",
+        "Delin Qu",
+        "Jaehong Yoon",
+        "Wentao Zhang"
+      ],
+      "published": "2026-05-14T17:59:04+00:00"
+    },
+    {
+      "id": "2605.15185v1",
+      "title": "Quantitative Video World Model Evaluation for Geometric-Consistency",
+      "url": "http://arxiv.org/abs/2605.15185v1",
+      "summary": "Generative video models are increasingly studied as implicit world models, yet evaluating whether they produce physically plausible 3D structure and motion remains challenging. Most existing video evaluation pipelines rely heavily on human judgment or learned graders, which can be subjective and weakly diagnostic for geometric failures. We introduce PDI-Bench (Perspective Distortion Index), a quantitative framework for auditing geometric coherence in generated videos. Given a generated clip, we obtain object-centric observations via segmentation and point tracking (e.g., SAM 2, MegaSaM, and CoTracker3), lift them to 3D world-space coordinates via monocular reconstruction, and compute a set of projective-geometry residuals capturing three failure dimensions: scale-depth alignment, 3D motion consistency, and 3D structural rigidity. To support systematic evaluation, we build PDI-Dataset, covering diverse scenarios designed to stress these geometric constraints. Across state-of-the-art video generators, PDI reveals consistent geometry-specific failure modes that are not captured by common perceptual metrics, and provides a diagnostic signal for progress toward physically grounded video generation and physical world model. Our code and dataset can be found at https://pdi-bench.github.io/.",
+      "authors": [
+        "Jiaxin Wu",
+        "Yihao Pi",
+        "Yinling Zhang",
+        "Yuheng Li",
+        "Xueyan Zou"
+      ],
+      "published": "2026-05-14T17:59:04+00:00"
+    }
+  ],
   "github_repos": [
     {
       "id": "626805178",