Action
-
Multi-Scale Latent Temporal Compression Attention and Video-Text Cross Attention Fusion for Video Action Detection
2026-10-21 04:08:49Yicheng Qiu, Keiji YanaiAbstract
In video action detection, modeling the temporal dependencies in continuous video streams is crucial. Previous methods based on deep neural networks can perform frame-level video recognition tasks. However, these methods struggle to model dynamic temporal patterns, facing challenges such as redundant frame information, difficulty balancing global and local features, and insufficient fusion of visual and textual features. To address these issues, we propose a video temporal attention module based on the Multi-Scale Latent Temporal Compression Attention, which aggregates continuous temporal sequences into feature representations across diverse temporal scales while efficiently reducing redundancy. We also introduce a Video-Text Cross Attention Fusion module that leverages cross-attention at both latent and standard feature levels, achieving fine-grained fusion between video and textual features to enhance semantic alignment and feature interaction. Experimental results across multiple video recognition benchmarks demonstrate that our proposed methods achieve superior performance compared to existing methods, validating the effectiveness and generalization capability.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29176
PDF
-
How Representational Priors Shape Generalization in World Action Models
Abstract
World action models (WAMs) are shown to substantially diverge under generalization performance based on their generative backbones. We investigate this gap through the representational geometry that WAMs inherit from their backbones and how policy training reorganizes it. Our central hypothesis is that a useful representational prior would structure geometry that separates nuisance information from the in-distribution manifold in advance of training as a policy. We quantify this with nuisance orthogonality; orthogonality of the feature under visual variation outside the empirical in-distribution manifold. Across various image and video backbones, we show that a higher orthogonality from the representational prior is associated with stronger robustness to visual variations after WAM training, enabling policy generalization measurable prospectively for world models. In addition, analyzing the resulting robust WAMs reveals nuisance suppression; relative suppression of feature displacement caused by such variations and robust action generation of the policy. Together, these findings provide a geometric perspective on how the representational prior of the world model shapes the generalization performance of WAMs.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29168
PDF
-
XingChen-UniFE: A Unified Generative Speech Front-End via Complementary Acoustic-Semantic Modeling
2026-10-21 03:55:18Kelan Kuang, Qihan Hu, Jian Kang, Jie Li, Yongxiang Li, Yi LiuAbstract
This paper presents XingChen-UniFE, a unified large audio language model (LALM) for general speech restoration (GSR) and target speaker extraction (TSE). The proposed model employs complementary semantic and acoustic representations through a dual-stream encoder, where a self-supervised learning model provides hierarchical semantic features and a learnable acoustic branch captures fine-grained structures from complex spectra. A bidirectional cross-attention module further facilitates information exchange between the two representations. The fused representation conditions the LALM for discrete speech token generation with task and speaker information. Experiments demonstrate strong performance on both GSR and TSE and robust generalization to unseen degradation conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29164
PDF
-
SimPT: An Automated Simulation Framework for Intervention-Based VLA Post-Training
2026-10-21 03:53:00Caicheng Wang, Zipeng Ye, Zhexuan Zhou, Zilong Huang, Xi Zhang, Yuchen XieAbstract
Human-in-the-loop (HIL) post-training improves vision-language-action (VLA) policies through expert corrections, but costly human supervision and repeated real-robot evaluation limit algorithm exploration and development. We introduce SimPT (Simulation-based Post-Training), a simulation framework that automates intervention-based VLA post-training by integrating policy rollouts, corrective data collection, policy training, and standalone evaluation. To automatically determine intervention timing and recovery strategies, we develop an Agent-Compiled Progress Graph that tracks task progress using privileged simulator state and retains stable snapshots during policy execution. After a rollout fails, a CuRobo-based expert attempts recovery from these snapshots, starting at more advanced task stages and falling back to earlier ones as needed to generate corrective trajectories. We implement and compare four post-training methods, RECAP, HG-DAgger, DPO+SFT, and FlowPRO, across seven RoboTwin manipulation tasks. Relative to a fixed-interval snapshot baseline, the Progress Graph reduces recovery attempts by 75.0% and increases recovery success from 93.4% to 98.9%. Using SimPT, we develop RECAP-CF-LPF, a refinement of RECAP that combines cross-fitting and low-pass filtering of value predictions. This refinement increases the overall success rate from 76.50% to 79.36% in held-out simulation trials. In real-robot HIL post-training, it increases the success rate from 72% to 96% across 25 scenarios of an Ethernet cable-insertion task. These results demonstrate the utility of SimPT for exploring and developing VLA post-training algorithms.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29161
PDF
-
SICAF: Time-Varying Focus Bottleneck for Self-Supervised Event-Based Optical Flow with Spiking Neural Network
2026-10-21 03:48:08Shuangming Yang, Qing He, Shangqi Guo, Badong ChenAbstract
Event-based optical flow estimation achieves high temporal resolution and dynamic range by exploiting asynchronous events, while self-supervised learning provides an effective solution to the absence of ground-truth labels. Spiking neural networks (SNNs), as brain-inspired models, process information through asynchronous spikes, naturally aligning with optical flow events. However, the temporal irregularity across event streams hinders the compact extraction of spatiotemporal features, while the absence of explicit labels leads to spurious correlations, thereby increasing redundancy within latent representations. In this paper, we propose a novel 'Compactly Compress, Time-varying Focus' training strategy. We present SICAF (Spikes Information Compression And Focus) framework that formulates the learning process as a constrained optimization with temporal modulation. It encourages self-supervised SNNs to extract compact latent representations while preventing over-compression that would discard temporal saliency. To better enable feature selectivity across time steps, we design an estimator with learnable temporal attention, achieving context awareness with cross-time dependencies. Experimental results on several datasets demonstrate that SICAF achieves state-of-the-art performance in self-supervised SNNs, with a 8.14% improvement in AEE on the MVSEC dataset and a 13.49% increase in robustness under white-box adversarial attacks.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29158
PDF
-
State-Dependent Objective Coordination for End-to-End Balance and Navigation
2026-10-21 03:24:37Xuhao Huang, Xitao Xu, Yuxin Zheng, Yunfan Ren, Zheng GongAbstract
Traditional robotic navigation systems separate mapping, planning, and low-level control through cascaded modules, introducing computational overhead and accumulated latency. End-to-end reinforcement learning offers a compact unified policy that maps onboard observations directly to control actions. However, on inherently unstable platforms such as two-wheeled inverted-pendulum robots, navigation progress and obstacle avoidance can conflict with attitude stabilization in a strongly state-dependent manner. We propose a lightweight state-dependent objective coordination method for integrated navigation and control. A pitch-conditioned scalarization coefficient $\beta(s)\in[0,1]$ adjusts the relative contributions of balance stabilization and navigation objectives during training, without extra control hierarchies or auxiliary attitude-stabilization controllers, such as proportional-integral-derivative (PID) or linear-quadratic regulator (LQR) controllers. To support zero-shot deployment, simulation training models execution delay, action hold, asynchronous and noisy observations, and dynamics uncertainty. Gradient analysis reveals persistent directional interference and state-dependent variation in the objectives' relative scales. Comparisons with alternative reward-weighting, constraint, training, and control-structure strategies show that PPO+$\beta(s)$, where PPO denotes proximal policy optimization, achieves 98% and 95% success rates with small attitude excursions on the fixed obstacle map and the randomly generated high-obstacle-density map, respectively. Zero-shot hardware experiments further demonstrate bounded attitude behavior and consistent obstacle avoidance using only onboard observations. Training and evaluation code will be made publicly available upon acceptance.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29142
PDF
-
Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
2026-10-21 03:20:32Jiongxiao Wang, Han Ding, Youzhi Luo, Yawei Wang, Yun Zhou, Linbo Liu, Panpan XuAbstract
Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they have no internalized knowledge of how that particular environment unfolds. A world model offers a natural remedy, providing the agent with environment specific knowledge by foreseeing how the environment will respond to the action it is about to take. Prior world models for LLM agents mainly predict next observations at each step, which is costly and often uninformative because individual observations provide limited task-relevant guidance and not every step requires call for foresight. This raises a key question: what a world model should predict and when should it be invoked for agentic tasks? Our pilot study suggests that world model guidance is most useful when it anticipates pitfalls within a bounded multi-step future. Motivated by this, we introduce Pitfall Anticipation World Model (PAWM), which selectively intervenes when a potential pitfall arises and predicts the impending failure to steer the agent away from it. PAWM is trained in two stages: Supervised Fine-Tuning (SFT) on synthetic data labeled with agent pitfalls, followed by GRPO co-training with a black-box LLM to further optimize it in the target environment. PAWM improves Claude Sonnet 4.5 performance by 24.3% on ScienceWorld and 7.7% on WebArena-Lite, outperforming a range of baselines. These results demonstrate that PAWM provides an effective form of world model guidance for black-box LLM agents.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29139
PDF
-
GRAFT: Restoring the Missing Sampling Interface in Continuous Diffusion Language Models
2026-10-21 03:15:30Haoning Yang, Lili PanAbstract
Continuous diffusion language models generate a whole sequence in parallel, never drawing a single word at any intermediate step; only the final step turns embeddings into text. Models such as ELF now report autoregressive-level quality at a fraction of the sampling steps of discrete-token diffusion models, and this design principle -- no step-wise discretization -- is what their own ablations credit for the gain. We show that this same principle carries a hidden cost: a collapse of the lexical long tail. Measured against held-out human text, ELF's output matches the human rate on common words almost exactly, yet its rarest words appear at only 0.30x the human rate, a gap that does not close from 105M to 652M parameters and that no sampler setting these models expose can reach. A same-size GPT-2, in contrast, spans zero to above the human rate by its decoding rule alone. We trace the difference to what continuous diffusion models lack: with no step that ever draws a discrete word, every step is instead driven by an average over the candidates still in play, and averaging a rare candidate together with the common word competing for its slot lands on the common word. An autoregressive model avoids this because its decoding rule samples one candidate rather than averaging over all of them. We propose GRAFT, a training-free method that grafts this missing choice back onto a frozen model: while the model is still undecided, GRAFT commits the rare words the average would otherwise discard, then regenerates the sequence from scratch with those commitments held in place. Across three ELF scales and on LangFlow, an independent continuous dLLM, GRAFT restores the rare-word share of generated text from 0.42-0.77x the human rate to 0.82-1.10x, a relative gain of 34-109%, and moves the entire word-frequency profile closer to human on every model tested, by 2.7x on ELF-B and 12x on LangFlow.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29136
PDF
-
LaVGuard: Selective Decoding for Contextual Value Drift
2026-10-21 03:14:15Jiale Dai, Liuxian Ma, Hongcan Deng, Xiaoke Niu, Wenjing Zhang, Zhaoxiang Liu, Shiguo Lian, Guojie SongAbstract
A content-safe response can still promote priorities that conflict with its task: an assistant may demand compliance when asked to preserve autonomy. We study contextual value drift as a task-relative decoding problem with an explicit allowance for change. LaVGuard separates an authorized ten-dimensional profile from a frozen estimate of the values expressed in the visible prefix. A profile-relative density gate selects when to evaluate candidate continuations; their signed progress determines a logit tilt constrained by both changed-position and cumulative-KL budgets. We establish local surrogate improvement, pathwise budget feasibility, and a bound on response-distribution change. Profile conditioning raises boundary TPR at 5% FPR from .58 to .72. At the same .12 mean intervention ratio, the selection study reports risk-safety scores of .88 for LaVGuard and .72 for position-matched edits. The resulting interface makes task priorities actionable while keeping the extent of intervention explicit and bounded.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29135
PDF
-
MuseFlow-TSE: Music-Aware Target Speech Extraction via Cross-Source Band-Splitting and Data-to-Data Flow Matching
2026-10-21 03:03:10Lu Ma, Yongliang Wang, Zhewen Su, Xin TianAbstract
Target speech extraction (TSE) isolates a speaker's voice given an enrollment utterance, but existing work is validated almost exclusively without music. Discriminative methods are content-faithful yet masking-bounded; generative methods improve naturalness but may hallucinate, drift in identity, and require many steps. We present MuseFlow-TSE, a discriminative deletion, generative insertion framework for music-bearing scenes. Stage-1 is a music-aware cross-source band-split extractor that estimates target, interfering speakers, music, and noise under conservation and music-leakage constraints. Stage-2 is a data-to-data flow refiner initialized by the discriminative estimate, negatively conditioned on interference stems, and run for 1--4 difficulty-adaptive Euler steps with an end-to-end fidelity objective.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29119
PDF
-
Learning to Absorb Corrections: Critic-Weighted Residual Flow for Vision-Based Insertion
2026-10-21 03:00:32Yiqing Yuan, Zhi Li, Jinrong Yang, Mingshan Sun, Mei Yu Zhi, Yong Zhao, Liangliang Ren, Hui ChengAbstract
Vision-based insertion requires correcting alignment errors while retaining useful behavior learned from demonstrations. We present ACT--Flow, an online learning framework that couples an adaptive Action Chunking Transformer (ACT) with a conditional residual flow policy. Following anchor-guided warmup, ACT absorbs successful executed actions through behavioral cloning, while residual targets are recomputed relative to the updated prior. A chunk-level critic learns from successful and failed interactions and evaluates final composed actions. Its estimates weight generated residual candidates for flow matching, complemented by supervision from successful real executions. Candidate scoring is confined to training; deployment uses RGB observations and samples one residual per action chunk. We evaluate the framework on power-plug, USB, and key insertion, together with cable routing and needle threading. Results show a five-task mean success rate of 66% at 20,000 post-initialization interaction steps, exceeding the strongest baseline by 18 percentage points at this checkpoint. Evaluations at matched checkpoints with the residual disabled show ACT's mean success increasing from 16% to 76%. Component ablations further support the complementary roles of behavior absorption, value weighting, and successful-action supervision.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29117
PDF
-
Agentic Video Understanding: A Survey
2026-10-21 02:49:27Xinyu Deng, Siwen Luo, Daochang LiuAbstract
As large language models (LLMs) become capable of processing increasingly diverse modalities and longer temporal contexts, an emerging line of work is moving beyond fixed video-language inference toward agentic systems that actively decide what information to inspect, retain, verify, and act upon. This survey reviews video understanding agents: systems that use video as the primary information source and solve understanding tasks through adaptive state construction and action selection. We first formalize an agent loop for video understanding, then address a central question: why do agents matter for video understanding? To answer this, we organize the literature through a challenge-to-design taxonomy, linking context bottlenecks to hierarchical evidence memory, evidence sparsity to active evidence acquisition, temporal causality to state and process tracking, and multimodal ambiguity to role-specialized coordination. We further review state space paradigms, learning paradigms, supervision signals, benchmarks, and evaluation protocols. Finally, we identify open directions toward agentic-native temporal modeling and video-native agents. Project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2609.31713
PDF
-
Causal world models under physical parameter shifts
Abstract
World models predict how embodied systems evolve under actions, but their accuracy can degrade when physical conditions move outside the training distribution. We study two components of causal modeling: which inputs a predictor may use, and how its mechanism computes their effects. In a synthetic benchmark with shortcut features, a frozen dependency mask derived from paired interventions reduces mean out-of-distribution (OOD) RMSE from 1.769 for a fully connected predictor to 0.697, close to the generator-oracle mask (0.694). For robot link-mass extrapolation in CausalWorld, with disjoint training and test mass ranges, we develop finger-local world models in which link masses enter inertia and dynamics-bias terms through explicit affine coefficients, actions act through either a learned torque network or the known PD controller, and learned residuals capture contact-related effects. Compared with a separately trained control whose mass inputs are fixed at training means, the known-PD model turns out-of-range masses into a consistent predictive gain: median time-averaged joint R2 over 150-step rollouts rises from 0.609 to 0.727, with improvement in all ten task-seed runs (exact two-sided sign test, p = 0.002). In a shared 100-step forecast, the learned-torque model is competitive with the strongest recurrent robotic world model (RWM) configuration (median joint R2 of 0.755 - 0.768 versus 0.752), whereas supplying out-of-range masses sharply degrades the MLP and recurrent predictors. These results indicate that how a physical parameter enters the dynamics mechanism determines whether its out-of-range value helps or harms prediction.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29088
PDF
-
Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
2026-10-21 01:58:00Ke Jiang, Wen Jiang, Yoshinobu Kawahara, Xiaoyang TanAbstract
We aim to address the problem of Out-of-Distribution (OOD) Mode Averaging, a critical problem that emerges when distilling Flow Matching (FM) based behavior policies in offline Reinforcement Learning (RL): When learning over multi-modal datasets, standard distillation inadvertently forces the policy to interpolate across different behavioral modes, causing the generated actions to collapse into invalid, OOD regions. To resolve this, we propose Value-Rectified Distillation (VRD), a novel framework that reformulates offline policy distillation as a value-guided generative trajectory alignment problem. By employing a dynamic coupling mechanism, VRD actively rectifies flow dynamics to explicitly decouple distinct behavioral modes, cleanly isolating high-value actions. Specifically, we instantiate VRD through two complementary algorithms: VRD-Q, which utilizes value-quantile behavior distillation to statistically filter out low-value behaviors, and VRD-R, which reorganizes the latent space to geometrically isolate high-value generative trajectories from the FM-based behavior policy. Extensive empirical evaluations demonstrate that VRD improves existing generative baselines across diverse continuous control and planning tasks on the D4RL, OGBench, and Minari benchmarks, especially over those multi-modal, non-expert datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29075
PDF
-
Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents
2026-10-21 01:27:11On Tai Tang, Huanyao Zhang, Jiepeng Zhou, Wentao ZhangAbstract
The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single task, an agent may repeatedly search the web, inspect visual evidence, revisit earlier hypotheses, and accumulate tens of thousands of tokens of multimodal context. Despite this trend, online RL for multimodal research agents remains largely confined to shorter contexts and interaction horizons. We push online RL training to 128k context and 75+ tool-interaction turns. To our knowledge, this is the first online multimodal deep-research RL study trained at 128k context, and the first trained with a 75 tool-turn horizon. Scaling to this regime exposes several practical limitations of conventional RL training. Early in training, weak policies make poor use of large interaction budgets, causing expensive rollouts with little reward improvement. Later, policy entropy can collapse before performance has saturated, prematurely ending useful learning. We introduce Long-MDR, a three-component training recipe designed specifically for this setting: On-Policy Distillation Warmup, Progressive Horizon Expansion, and Entropy-Triggered Rescue. Together, these techniques improve both the learning efficiency and stability of long-horizon RL, enabling continued gains in a regime where direct training is slow and costly. Code will be available at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2610.05195
PDF
-
Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed Language
2026-10-21 01:22:21Vedanth Kallakuri, Katherine A. SkinnerAbstract
Understanding the properties of fish populations is increasingly important. Analyzing fish length, width, and mass is integral to maintaining healthy stock in the aquaculture industry, while counting wild fish populations supports marine conservation and coastal reef restoration efforts. Deep learning-based segmentation models offer a promising means of obtaining such information at scale. Most of these models, however, require costly and time-consuming per-pixel segmentation labels for training, which can take up to two minutes per fish to annotate manually. To address this inefficiency, we propose a weakly supervised segmentation pipeline that requires only point-labeled data (one click per fish) to produce accurate segmentation masks. Our pipeline consists of four stages. First, a point-supervised tracker predicts point annotations for each frame. These points are then converted into motion-informed language expressions, which guide a language-conditioned segmentation model (LMPM++) to capture temporal consistency and produce candidate segmentation masks. Finally, optical flow and background subtraction are applied as post-processing filters to suppress noise from incorrect segmentations. Experiments on the DeepFish dataset show improvement in foreground mean intersection over union (mIoU) as compared to baseline models.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29047
PDF
-
Resonance and Depth: Composing Timbral Identity and Spatial Form in an Artist-in-the-Loop Co-Creative Multi-Agent Percussive Performance
Abstract
This research-creation artistic report presents Resonance and Depth, a 9-minute immersive electroacoustic/acousmatic performance that treats timbre and space as primary compositional materials. Built from live electronic percussion and a personal corpus recorded, curated, and composed by the author, the work investigates how timbral identity and musical form can emerge through real-time recombination, spatialisation, and performer-system interaction rather than conventional harmonic development. The performance is realised as an artist-in-the-loop co-creative multi-agent system integrating MACAT, MACataRT, a MIDI-based musical agent, a spatialisation layer, and a conductor agent. These components recombine and transform the performer's material through concatenative synthesis, audio mosaicing, symbolic mediation, physically inspired synthesis, and object-based spatialisation. The work approaches spatialisation as an active compositional process in which position, trajectory, depth, diffusion, width, and apparent size participate in musical phrasing and form. Through this practice-based framework, Resonance and Depth examines how performer identity can be retained, transformed, and redistributed across human, algorithmic, timbral, and spatial processes within an immersive co-creative performance ecology.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29026
PDF
-
GenXR-Bench: Benchmarking Coding Agents for Interactive XR Development
2026-10-21 00:28:16William Huang, David Li, Xiang 'Anthony' Chen, Xun Qian, Nels Numan, Zhongyi Zhou, Hyunsung Cho, Salman Muin Kayser Chishti, Ding Xia, Xincheng Huang, Jiahao Ren, Faraz Faruqi, Tianyu Xu, Yang Zhang, David Kim, Ruofei DuAbstract
Coding agents are rapidly advancing from single-file generation to long-horizon repository-scale engineering, yet the evaluation of their capabilities to author interactive extended reality (XR) applications remains an open challenge. Unlike traditional software, XR applications operate within dynamic 3D physical environments where runtime robustness depends jointly on continuous user motion, spatial input, and environmental sensing. We introduce GenXR-Bench, the first benchmark for the end-to-end evaluation of coding agents in XR software development. GenXR-Bench contains 112 expert-authored tasks spanning 24 feature implementations, 21 bug repairs, and 67 application generation tasks. Held-out tests combine scripted and adaptive interactions with programmatic and visual assertions to evaluate core functionality and runtime behavior. GenXR-Bench's testing suite achieves 96.5% accuracy against blinded expert judgments of 124 candidate applications executed on consumer XR hardware. Evaluating 10 models on GenXR-Bench, we find that the strongest configuration achieves only 50.9% overall Pass@1. Failure analysis identifies recurring misunderstandings in asynchronous sensor lifecycles, physical grounding, and embodied interaction semantics. GenXR-Bench establishes a reproducible testbed for evaluating coding agents in the interactive and physically situated conditions of spatial computing.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29014
PDF
-
An Evidence-Driven Multi-Agent Compound AI System for Head and Neck Proton Radiotherapy Planning
2026-10-21 00:21:19Qingqing Wang, Liqiang Xiao, Chang ChangAbstract
Radiation treatment planning requires balancing competing objectives to satisfy clinical criteria while accommodating physicians' preferences. This study aims to develop an evidence-driven, multi-agent compound AI system that integrates DRL-based automated treatment planning with LLM-based conversational plan refinement for proton PBS therapy. A policy network trained using PPO first generates multiple plans without human intervention, from which one is selected as the target plan and the others serve as references for subsequent refinements. For challenging cases requiring further adjustments, an LLM-based multi-agent system enables plan refinement through natural language interactions. The system comprises a planning agent that recommends evidence-supported adjustments, a chatbot providing a unified conversational interface, and an evaluation agent that retrieves clinical metrics and identifies constraint violations. To enhance domain-specific reasoning, the planning agent based on Qwen3-14B is designed to justify its recommendations using evidence from previously generated plans and post-trained on task-specific data using SFT and GRPO. The framework was trained and evaluated using 21 patients with H&N cancers, with 5 PPO-generated plans per patient. Of these, 50 plans were selected for instruction-guided refinement. Compared with the base LLM, the post-trained Qwen3-14B demonstrated improved instruction-following, information retrieval and domain-specific reasoning capabilities. The resulting adjustments more effectively improved user-prioritized objectives while limiting deterioration in competing objectives, yielding more clinically appropriate trade-offs in patient-specific plan refinement. The proposed compound AI system provides a user-friendly planning platform, offering a promising approach to personalized proton PBS treatment planning.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29010
PDF
-
T-DIG: Text-Conditioned Diversity for Image Generation
2026-10-21 00:11:32Simran Khanuja, Rohit Gandikota, Gaurav Parmar, Graham NeubigAbstract
Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent. However, T2I models are usually optimized for single-image quality which comes at a cost of multi-image diversity, limiting this exploration. Diversity is also contextual: one user wants variations in background, while another wants them in artistic style, and the same user may later want variations in clothing. The generated images should differ along whatever axis the user cares about. In this paper, we introduce T-DIG, a text-conditioned diversity framework for image generation and editing. T-DIG is an iterative framework comprising multiple modules that lets users type prompts, inspect default generations, request variations using natural language, and continue refining outputs until they're satisfied. At its core, T-DIG uses an LLM policy that produces K axis-conditioned prompts or edit instructions for a frozen generator or editor. We train this policy with GRPO using a text-conditioned diversity reward and alignment penalties. Because the image generator remains frozen, T-DIG applies to open-weight, distilled, and proprietary models. We also release the first dataset for text-conditioned prompt diversity in image generation, with the training and validation splits built from human-written axes and a test set drawn from real user interactions with T-DIG. T-DIG far exceeds baselines in improving text-conditioned diversity, while preserving base-prompt alignment across a variety of metrics and human judgements. We show that user-directed diversity exploration can be added to a wide range of image generation models, open and closed, without the need to access their weights. Website: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2610.29003
PDF
