4arXiv cs.AI (Artificial Intelligence)·
ElasticTTT: Framework to Prevent Prior Collapse in Test-Time Tuning for Video Editing
Researchers introduce ElasticTTT, a framework addressing a fundamental failure mode called 'Prior Collapse' in test-time tuning (TTT) of diffusion models for video editing, where models discard text conditions and collapse to the source video. The approach combines three techniques: Target Distribution Regularization, Contrastive CFG, and Asynchronous Noise Schedule to preserve the generative prior during single-shot optimization. Evaluations claim state-of-the-art performance on one-shot video editing benchmarks.
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TunerDiT: Training-free Progressive Steering of Diffusion Transformers for Multi-Event Video Generation
TunerDiT is a training-free method for steering video diffusion transformers (DiTs) to generate long-horizon videos containing multiple sequential events. The approach identifies intrinsic turning points in the DiT denoising trajectory where text conditioning shifts from global layout to fine-grained detail, then applies two steering mechanisms: Event-Partitioned Masking and Cross-Event Prompt Fusion. The authors also introduce Meve, a benchmark prompt suite for multi-event video generation, and report state-of-the-art results across 8 metrics with improved text alignment scaling with event count.
Test-Time Training End-to-End (TTT-E2E) Retrains Model Weights to Handle Long Inputs
Researchers from Astera Institute, Nvidia, Stanford, UC Berkeley, and UC San Diego introduced TTT-E2E, a method that compresses long context into transformer weights by training the model during inference via meta-learning. The approach uses sliding-window attention restricted to 8,000 tokens and updates only the fully connected layers of the last quarter of the network on each 1,000-token chunk at inference time, keeping per-token generation latency roughly constant as context scales to 128,000 tokens. TTT-E2E slightly outperforms vanilla transformers on next-token prediction loss across long contexts and matches efficient architectures like Mamba 2 and Gated DeltaNet on inference speed, but fails dramatically on Needle-in-a-Haystack retrieval beyond 8,000 tokens and incurs substantially higher training latency. The work reframes long-context handling as a training-inference trade-off rather than an architectural design problem.
HullFT: Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching
HullFT is a new method for test-time finetuning (TTFT) of language models that addresses the dual bottlenecks of retrieval quality and per-query finetuning cost. It represents query embeddings as sparse convex combinations of training sequences using Frank-Wolfe optimization, yielding diverse and relevant support sets without expensive diversity-aware search. A geometric integerization step converts fractional weights into integer multiplicities, enabling a Gradient Reuse scheme that amortizes forward-backward computation across repeated examples. Experiments show improved quality-efficiency tradeoffs over prior TTFT methods, measured in bits-per-byte at lower total runtime.
E-TTS: Embodied test-time scaling framework for robotic manipulation with history-aware iterative refinement
Researchers introduce E-TTS, a modular test-time scaling framework for robotic manipulation that unifies reasoning and action scaling via history-aware iterative refinement and vision-language verifiers. The framework addresses two gaps in prior work: underexplored reasoning scaling mechanisms and inadequate use of historical context in long-horizon sequential tasks. Evaluated across 4 benchmarks, 6 environments, 3 embodiments, and 4 base vision-language-action models, E-TTS achieves up to 33.14% improvement in simulation and 26.62% in real-world scenarios without additional expert data or retraining.
TopoTTA integrates persistent homology into test-time adaptation for anomaly segmentation
Researchers introduce TopoTTA, a framework that incorporates persistent homology from topological data analysis into test-time adaptation (TTA) for anomaly segmentation. The method applies multi-level cubical complex filtration to anomaly score maps to generate topological pseudo-labels that guide a lightweight test-time classifier, avoiding pixel-level heuristics like confidence thresholding. Evaluated across six benchmarks including MVTec AD, VisA, and MVTec 3D-AD, TopoTTA achieves an average 15% F1 improvement over state-of-the-art unsupervised methods, with the largest gains on geometrically complex defects.
Self-Guided Test-Time Training improves long-context LLM utilization by up to 15%
Researchers propose Self-Guided Test-Time Training (S-TTT), a method that addresses the degradation in accuracy LLMs exhibit on long inputs by having the model first identify relevant evidence spans before applying test-time training only to those spans. A preliminary study on LongBench-v2 shows that TTT on randomly sampled spans hurts performance while TTT on oracle spans substantially helps, motivating the self-guided selection approach. Evaluated on LongBench-v2 and LongBench-Pro with Qwen3-4B-Thinking-2507 and Llama-3.1-8B-Instruct, S-TTT achieves up to 15% relative accuracy improvement. The method offers a practical path to better long-context utilization without the prohibitive cost of full-context adaptation.
TEVI: Sparse autoencoders for text-conditioned editing of CLIP image embeddings to improve vision-language alignment
TEVI is a framework that uses sparse autoencoders to disentangle CLIP image embeddings and a learned masking module to selectively reconstruct embeddings conditioned on a given caption, addressing the information imbalance between images and their captions. The approach improves image-text retrieval on both coarse-grained benchmarks (MS COCO, Flickr) and fine-grained long-caption benchmarks (IIW, DOCCI), with larger gains on richer captions. The work also shows improved robustness on the RoCOCO benchmark.
Match Task to Objective (MTO) framework for aligning fine-tuning and prompt-tuning strategies with encoder-decoder pre-training objectives
A new arXiv preprint introduces the Match Task to Objective (MTO) framework, which systematically aligns fine-tuning and prompt-tuning strategies with the pre-training objectives of encoder-decoder language models. The work focuses on commonsense knowledge retrieval and generation tasks, reporting over 120% performance gains versus conventional methods in few-shot settings. The framework includes automated methods for preparing task-related data via unsupervised adaptation and novel template design for soft prompt engineering.