Westlake University

Vision-language-action models usually predict a whole future action chunk in one pass, and adjacent chunks lack explicit temporal consistency constraints, so actions can jump at chunk boundaries. ODEAction recasts generation as continuous evolution from the current action: it learns an action transition field and integrates it with an ordinary differential equation to produce subsequent actions, instead of jointly predicting a fixed-length chunk. Average success rate rises from 96.85% to 98.25% on LIBERO and from 15.28% to 24.72% on EBench, a 61.8% relative gain; on five RoboTwin tasks it reaches 84.4% average success against the 82.4% baseline.

Vision-language-action models usually predict a whole future action chunk in one pass, and adjacent chunks lack explicit temporal consistency constraints, so actions can jump at chunk boundaries. ODEAction recasts generation as continuous evolution from the current action: it learns an action transition field and integrates it with an ordinary differential equation to produce subsequent actions, instead of jointly predicting a fixed-length chunk. Average success rate rises from 96.85% to 98.25% on LIBERO and from 15.28% to 24.72% on EBench, a 61.8% relative gain; on five RoboTwin tasks it reaches 84.4% average success against the 82.4% baseline.

Multi-view 3D vision-language models suffer viewpoint-induced semantic drift: the same object and query produce weaker, diffuse or shifted target responses when the camera moves. LieVLM has geometry tokens predict local rotation parameters and channel-wise gates, applies length-preserving rotations to projected visual features within learned three-dimensional subspaces, and gates the identity-relative correction back into the original representation. On ScanRefer, LieVLM-8B reaches 64.5 Acc@0.25 and 57.2 Acc@0.5, 6.9 and 6.3 points above the published VG LLM-8B under the same proposal-refinement protocol.

Multi-view 3D vision-language models suffer viewpoint-induced semantic drift: the same object and query produce weaker, diffuse or shifted target responses when the camera moves. LieVLM has geometry tokens predict local rotation parameters and channel-wise gates, applies length-preserving rotations to projected visual features within learned three-dimensional subspaces, and gates the identity-relative correction back into the original representation. On ScanRefer, LieVLM-8B reaches 64.5 Acc@0.25 and 57.2 Acc@0.5, 6.9 and 6.3 points above the published VG LLM-8B under the same proposal-refinement protocol.

Shanghai Jiao Tong University and collaborators assembled a roughly 47,000-hour multisource pool and extended a pretrained video model's language–video priors into language–video–action learning, yielding the world action model SWAM. On the 50 manipulation tasks of RoboTwin 2.0, SWAM reaches 61.56% success under the randomized Hard setting, 12.86 percentage points above the strongest baseline OpenWAM, and 88.10% under the clean Easy setting, slightly below OpenWAM's 89.30%. On a real bimanual platform, its three-task average Hard success rate is 40.00% against OpenWAM's 23.33%.

Shanghai Jiao Tong University and collaborators assembled a roughly 47,000-hour multisource pool and extended a pretrained video model's language–video priors into language–video–action learning, yielding the world action model SWAM. On the 50 manipulation tasks of RoboTwin 2.0, SWAM reaches 61.56% success under the randomized Hard setting, 12.86 percentage points above the strongest baseline OpenWAM, and 88.10% under the clean Easy setting, slightly below OpenWAM's 89.30%. On a real bimanual platform, its three-task average Hard success rate is 40.00% against OpenWAM's 23.33%.

Conventional database searching assigns high-confidence peptides to only about 20% to 31% of acquired spectra, leaving the rest unread. π-SPECFormer was pre-trained on 291 million tandem mass spectra in two stages: self-supervised learning of fragmentation patterns, then refinement with peptide–spectrum match groupings. It beat task-specific baselines in de novo sequencing, spectrum clustering and modification detection, and in 103 lung adenocarcinoma samples it produced nine high-confidence mutation peptides, six of them absent from the public databases examined.

Conventional database searching assigns high-confidence peptides to only about 20% to 31% of acquired spectra, leaving the rest unread. π-SPECFormer was pre-trained on 291 million tandem mass spectra in two stages: self-supervised learning of fragmentation patterns, then refinement with peptide–spectrum match groupings. It beat task-specific baselines in de novo sequencing, spectrum clustering and modification detection, and in 103 lung adenocarcinoma samples it produced nine high-confidence mutation peptides, six of them absent from the public databases examined.

In automated research ideation, the step of finding a limitation that later work can actually pick up has lacked a direct supervision signal. ActLim-Bench links each source paper's limitation to the later citing work that adopted it as motivation and tried to address it, yielding 18,279 evidence-linked triples. ActLim-8B, post-trained in three stages on that signal, reaches 0.452 graded recall on a temporally held-out 2024–2025 NLP test set against 0.262 for the strongest zero-shot baseline, and transfers from NLP-only training to computer vision at 0.314 versus 0.252.

In automated research ideation, the step of finding a limitation that later work can actually pick up has lacked a direct supervision signal. ActLim-Bench links each source paper's limitation to the later citing work that adopted it as motivation and tried to address it, yielding 18,279 evidence-linked triples. ActLim-8B, post-trained in three stages on that signal, reaches 0.452 graded recall on a temporally held-out 2024–2025 NLP test set against 0.262 for the strongest zero-shot baseline, and transfers from NLP-only training to computer vision at 0.314 versus 0.252.

Few-step text-to-image generation must render prompt details with very few sampling steps, and distribution matching distillation (DMD) relies on learned score differences whose statistics need not align. Intrinsic Distribution Matching keeps the full DMD correction and adds a moment constraint on the means and per-coordinate standard deviations of the generator's exponential-moving-average features, with no external feature encoder. On Z-Image-Turbo 6B at two network evaluations it reaches 94.25% and 90.20% on Chinese and English LongText-Bench, the highest scores among the compared methods at that budget.

Few-step text-to-image generation must render prompt details with very few sampling steps, and distribution matching distillation (DMD) relies on learned score differences whose statistics need not align. Intrinsic Distribution Matching keeps the full DMD correction and adds a moment constraint on the means and per-coordinate standard deviations of the generator's exponential-moving-average features, with no external feature encoder. On Z-Image-Turbo 6B at two network evaluations it reaches 94.25% and 90.20% on Chinese and English LongText-Bench, the highest scores among the compared methods at that budget.

In multimodal long-document question answering, agentic retrieval is not always worth its extra computation and can even hurt answer accuracy. RouteDRAG makes both whether to invoke an agent and how it then executes into learned choices: a graph-backed index organizes cross-page, table and figure content, path routing predicts the benefit of an agent over shorter paths, and dynamic execution controls tool use and termination. Across two benchmarks it assigns only 79.3% of queries to an agent while averaging 3.47 percentage points higher accuracy than shorter-path-only execution, and it also surpasses uniform agent execution with fewer answering calls.

In multimodal long-document question answering, agentic retrieval is not always worth its extra computation and can even hurt answer accuracy. RouteDRAG makes both whether to invoke an agent and how it then executes into learned choices: a graph-backed index organizes cross-page, table and figure content, path routing predicts the benefit of an agent over shorter paths, and dynamic execution controls tool use and termination. Across two benchmarks it assigns only 79.3% of queries to an agent while averaging 3.47 percentage points higher accuracy than shorter-path-only execution, and it also surpasses uniform agent execution with fewer answering calls.

Applying the same visual-residual edit at every decoder layer with matched RMS strength reveals a persistent drop in effect, called the Evidence Anchoring Boundary; routing interventions by question type and calibration improves full-set attribute accuracy across three checkpoints, while universal subtraction is neutral or harmful.

Applying the same visual-residual edit at every decoder layer with matched RMS strength reveals a persistent drop in effect, called the Evidence Anchoring Boundary; routing interventions by question type and calibration improves full-set attribute accuracy across three checkpoints, while universal subtraction is neutral or harmful.