Beijing University of Posts and Telecommunications

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.

RGB-X tracking pairs visible frames with thermal, depth or event data, but paired annotations are far scarcer than RGB pretraining data, so only a small set of parameters is usually tuned. Earlier bidirectional cross-modal corrections computed both directions side by side from uncorrected input states, so the second correction could not use the first one's result. CCLT inserts low-rank exchange modules at blocks 6 and 10 of a frozen backbone, letting one direction correct first and the other read the freshly corrected state; against a parameter-matched parallel version, mean LasHeR SR rises by 1.980 points (95% interval [0.360, 2.690]), with 196.6K trainable parameters, 0.212% of the 92.7M-parameter tracker.

RGB-X tracking pairs visible frames with thermal, depth or event data, but paired annotations are far scarcer than RGB pretraining data, so only a small set of parameters is usually tuned. Earlier bidirectional cross-modal corrections computed both directions side by side from uncorrected input states, so the second correction could not use the first one's result. CCLT inserts low-rank exchange modules at blocks 6 and 10 of a frozen backbone, letting one direction correct first and the other read the freshly corrected state; against a parameter-matched parallel version, mean LasHeR SR rises by 1.980 points (95% interval [0.360, 2.690]), with 196.6K trainable parameters, 0.212% of the 92.7M-parameter tracker.

FDE-Bench turns 49 real customer projects into end-to-end delivery tasks where agents must elicit unstated requirements from a simulated customer and iterate, and across 18 agent configurations the highest mean delivery score is only 0.5510 against an FDE reference of 1.0.

FDE-Bench turns 49 real customer projects into end-to-end delivery tasks where agents must elicit unstated requirements from a simulated customer and iterate, and across 18 agent configurations the highest mean delivery score is only 0.5510 against an FDE reference of 1.0.

Instead of a fixed intervention chosen in advance, Q-NeuS reads each query's own internal activations to decide which feed-forward neurons to amplify, improving every one of seven models from 3B to 32B parameters across three benchmarks.

Instead of a fixed intervention chosen in advance, Q-NeuS reads each query's own internal activations to decide which feed-forward neurons to amplify, improving every one of seven models from 3B to 32B parameters across three benchmarks.

Multi-agent memory is reorganized around task dependency graphs: a shared blackboard records evidence during execution and corrects goal, plan, and world-state divergence, then verified steps and dependencies are distilled into reusable procedures, giving the highest task score in all six benchmark settings across ALFWorld, WebArena, and OfficeBench.

Multi-agent memory is reorganized around task dependency graphs: a shared blackboard records evidence during execution and corrects goal, plan, and world-state divergence, then verified steps and dependencies are distilled into reusable procedures, giving the highest task score in all six benchmark settings across ALFWorld, WebArena, and OfficeBench.

Model protection for on-orbit inference no longer has to be all-or-nothing. STAR-OV applies a two-stage reversible perturbation in parameter space, degrading a model from original accuracy to a medium-accuracy state and then to near-random guessing, while the recorded perturbations form hierarchical keys that restore different performance levels by authorization tier. A latent watermark embedded after recovery stays inactive during normal inference and produces verifiable trigger responses once activated. Across four public datasets, the fully encrypted state drops to roughly 10% to 13.97% accuracy while only 10⁻³% to 10⁻²% of parameters are modified.

Model protection for on-orbit inference no longer has to be all-or-nothing. STAR-OV applies a two-stage reversible perturbation in parameter space, degrading a model from original accuracy to a medium-accuracy state and then to near-random guessing, while the recorded perturbations form hierarchical keys that restore different performance levels by authorization tier. A latent watermark embedded after recovery stays inactive during normal inference and produces verifiable trigger responses once activated. Across four public datasets, the fully encrypted state drops to roughly 10% to 13.97% accuracy while only 10⁻³% to 10⁻²% of parameters are modified.

Multimodal LLMs often report no text in images whose coarse structure carries a string a human recovers by squinting, and a training method that reads the original image alone lifts transcription from 26.50 to 88.61 on production moderation images.

Multimodal LLMs often report no text in images whose coarse structure carries a string a human recovers by squinting, and a training method that reads the original image alone lifts transcription from 26.50 to 88.61 on production moderation images.

RAG-Stress introduces a controlled diagnostic protocol that holds the question and reference answer fixed, edits one assertion so the evidence supports a designated incorrect answer, and crosses two source-priority policies with three positions of the answer span in the evidence text, measuring a misleading rate on questions each model answered correctly without retrieval across fifteen systems, three QA datasets, and English and Chinese medical QA. Instructions that prioritize documents consistently produce higher misleading rates than those permitting prior knowledge, with a mean gap of 10.9–13.5 percentage points across the three QA datasets, and model-averaged rates follow End, Beginning, Middle under both policies.

RAG-Stress introduces a controlled diagnostic protocol that holds the question and reference answer fixed, edits one assertion so the evidence supports a designated incorrect answer, and crosses two source-priority policies with three positions of the answer span in the evidence text, measuring a misleading rate on questions each model answered correctly without retrieval across fifteen systems, three QA datasets, and English and Chinese medical QA. Instructions that prioritize documents consistently produce higher misleading rates than those permitting prior knowledge, with a mean gap of 10.9–13.5 percentage points across the three QA datasets, and model-averaged rates follow End, Beginning, Middle under both policies.

The work introduces OmniConfess, a training-free method that fixes a candidate response and re-scores it at token resolution under controlled channel-wise evidence interventions, yielding a structured token-by-channel confession that reveals the response's evidential dependence; it uses this confession to preserve grounded content and correct commitments driven by irrelevant or contradictory evidence, and it constructs OmniHalluBench, a 3,540-example benchmark built from six datasets spanning text, image, audio, and video settings and both judgment and free-form generation, with experiments showing hallucination mitigation across heterogeneous modality and task settings.

The work introduces OmniConfess, a training-free method that fixes a candidate response and re-scores it at token resolution under controlled channel-wise evidence interventions, yielding a structured token-by-channel confession that reveals the response's evidential dependence; it uses this confession to preserve grounded content and correct commitments driven by irrelevant or contradictory evidence, and it constructs OmniHalluBench, a 3,540-example benchmark built from six datasets spanning text, image, audio, and video settings and both judgment and free-form generation, with experiments showing hallucination mitigation across heterogeneous modality and task settings.