University of Electronic Science and Technology of China

In a retrospective cohort of 1,286 men with non-obstructive azoospermia at two reproductive medicine centers, intraoperative AI-SpermScope marking of likely spermatogenic tubules and rapid sperm confirmation raised sperm retrieval success to 68.1% versus 47.6% for conventional micro-TESE, a gap that persisted after propensity score matching. Among men with retrieved sperm, the subsequent ICSI live birth rate per transfer cycle was 38.5% in the AI group and 26.8% in the conventional group; group assignment was not randomized, so a causal effect still needs prospective randomized trials.

In a retrospective cohort of 1,286 men with non-obstructive azoospermia at two reproductive medicine centers, intraoperative AI-SpermScope marking of likely spermatogenic tubules and rapid sperm confirmation raised sperm retrieval success to 68.1% versus 47.6% for conventional micro-TESE, a gap that persisted after propensity score matching. Among men with retrieved sperm, the subsequent ICSI live birth rate per transfer cycle was 38.5% in the AI group and 26.8% in the conventional group; group assignment was not randomized, so a causal effect still needs prospective randomized trials.

Chest X-ray report generation now defines its generation units directly as the anatomical region sets that statements are linked to in report annotations, rather than preset topics or single regions. RadCarve has a planner predict from the image which units exist and which regions each contains, then generates each unit's text independently from the joint visual evidence of that region set. On Chest ImaGenome, given region sets it improves CXRFEScore by 9.4% over strong baselines, and its end-to-end binding F1@0.5 is 0.451 versus 0.185 for RGRG and 0.089 for ChEX, with 0.761B inference parameters.

Chest X-ray report generation now defines its generation units directly as the anatomical region sets that statements are linked to in report annotations, rather than preset topics or single regions. RadCarve has a planner predict from the image which units exist and which regions each contains, then generates each unit's text independently from the joint visual evidence of that region set. On Chest ImaGenome, given region sets it improves CXRFEScore by 9.4% over strong baselines, and its end-to-end binding F1@0.5 is 0.451 versus 0.185 for RGRG and 0.089 for ChEX, with 0.761B inference parameters.

Unsafe deserialization lets attacker-controlled data trigger chains of existing methods and reach code execution, while gateway, application and system controls each see only part of the attack. Janus runs a black-box attacker agent against an isolated replica to surface exploit variants, has a defender agent summarize each verified variant into security-relevant effects, and uses counterfactual replay to separate complementary coverage from redundant enforcement before jointly selecting and scoping controls. Across 16 deployments in six Java and PHP applications, held-out attack success falls to 6.2% versus 18.8%–55.0% for gateway, application, system and cross-layer baselines, while 98.4% of benign functionality tests pass.

Unsafe deserialization lets attacker-controlled data trigger chains of existing methods and reach code execution, while gateway, application and system controls each see only part of the attack. Janus runs a black-box attacker agent against an isolated replica to surface exploit variants, has a defender agent summarize each verified variant into security-relevant effects, and uses counterfactual replay to separate complementary coverage from redundant enforcement before jointly selecting and scoping controls. Across 16 deployments in six Java and PHP applications, held-out attack success falls to 6.2% versus 18.8%–55.0% for gateway, application, system and cross-layer baselines, while 98.4% of benign functionality tests pass.

A world action model can regenerate complete rollouts of previously learned tasks from their instructions alone, and after truncation and physical-consistency filtering, replaying them cuts the forgetting of sequential fine-tuning by over 90% on four LIBERO suites and five real-world tasks without keeping any data from previous tasks.

A world action model can regenerate complete rollouts of previously learned tasks from their instructions alone, and after truncation and physical-consistency filtering, replaying them cuts the forgetting of sequential fine-tuning by over 90% on four LIBERO suites and five real-world tasks without keeping any data from previous tasks.

Evo-World adds optical-flow-supervised future motion prediction to a lightweight vision-language-action policy, reaching 91.1% tier-averaged success on Meta-World MT50, a 13.0% relative gain over the non-predictive baseline, and beating the evaluated baselines on AgileX single-arm and bimanual tasks with 0.87B parameters.

Evo-World adds optical-flow-supervised future motion prediction to a lightweight vision-language-action policy, reaching 91.1% tier-averaged success on Meta-World MT50, a 13.0% relative gain over the non-predictive baseline, and beating the evaluated baselines on AgileX single-arm and bimanual tasks with 0.87B parameters.

Isolating the reinforcement-learning-stage parameter update of a language model, keeping only the single dominant direction of each matrix rescaled to the original magnitude, and injecting it into the language modules of a vision-language model improves matched full-update interpolation in 12 of 15 backbone-benchmark comparisons across three model families and five visual-reasoning benchmarks, including an 8.55 percentage-point MathVision gain on the Qwen recipient. Controls show that matching update magnitude or using arbitrary low-rank directions does not reproduce these gains, indicating that what is acquired during post-training and what remains transferable across models are not the same thing.

Isolating the reinforcement-learning-stage parameter update of a language model, keeping only the single dominant direction of each matrix rescaled to the original magnitude, and injecting it into the language modules of a vision-language model improves matched full-update interpolation in 12 of 15 backbone-benchmark comparisons across three model families and five visual-reasoning benchmarks, including an 8.55 percentage-point MathVision gain on the Qwen recipient. Controls show that matching update magnitude or using arbitrary low-rank directions does not reproduce these gains, indicating that what is acquired during post-training and what remains transferable across models are not the same thing.

The work presents ProsaBuddy, an LLM-based agent system that uses a ReAct loop with retrieval over the Prosa codebase, access to Rocq tools and optional human-written hints, and a subgoal-delegation architecture that decomposes a lemma into subgoals dispatched to subagents for proof, and on a mini benchmark drawn from real-time scheduling literature it significantly outperforms state-of-the-art LLM-based Rocq automated proving agent systems and the general coding agent OpenCode.

The work presents ProsaBuddy, an LLM-based agent system that uses a ReAct loop with retrieval over the Prosa codebase, access to Rocq tools and optional human-written hints, and a subgoal-delegation architecture that decomposes a lemma into subgoals dispatched to subagents for proof, and on a mini benchmark drawn from real-time scheduling literature it significantly outperforms state-of-the-art LLM-based Rocq automated proving agent systems and the general coding agent OpenCode.