Beijing Normal University

Non-prefix KV-cache reuse encodes retrieved chunks separately and concatenates their caches, losing 19% to 100% of context-dependent questions that full prefill answers, with same-kind distractor chunks adding to the loss. Comparing whether a small repair budget should go to distractors or to the answer-bearing chunk, the study finds that restoring all distractors costs nearly a full prefill, while recomputing the answer-bearing chunk beats token-level selectors at a 5% budget in all nine open-domain QA and API-documentation settings.

Non-prefix KV-cache reuse encodes retrieved chunks separately and concatenates their caches, losing 19% to 100% of context-dependent questions that full prefill answers, with same-kind distractor chunks adding to the loss. Comparing whether a small repair budget should go to distractors or to the answer-bearing chunk, the study finds that restoring all distractors costs nearly a full prefill, while recomputing the answer-bearing chunk beats token-level selectors at a 5% budget in all nine open-domain QA and API-documentation settings.

Web platforms must classify new pages before anyone labels them, large language model labels per page are noisy, and links mean opposite things across sites: online stores often join same-category products, while university sites mostly join pages of different classes. ZeroPolar calls the sign with which links enter a prediction structural polarity, reads that sign without true labels from the excess agreement of LLM labels and text-prototype predictions along edges, and applies one signed propagation step. With one GPT-4o label per page, ZeroPolar-T reaches the highest eleven-graph mean, 73.99%, 6.01 points above LLM-BP.

Web platforms must classify new pages before anyone labels them, large language model labels per page are noisy, and links mean opposite things across sites: online stores often join same-category products, while university sites mostly join pages of different classes. ZeroPolar calls the sign with which links enter a prediction structural polarity, reads that sign without true labels from the excess agreement of LLM labels and text-prototype predictions along edges, and applies one signed propagation step. With one GPT-4o label per page, ZeroPolar-T reaches the highest eleven-graph mean, 73.99%, 6.01 points above LLM-BP.

Agentic reinforcement learning shifts the reasoning trajectories of large language models, and trajectory-based confidence estimators trained on a base model lose much of their ranking ability after that shift. StaR-TTA treats trajectory stability as a label-free reliability signal: it learns the link between stability and correctness on labeled base trajectories, then uses stability rankings from unlabeled RL trajectories to correct confidence at test time. Across four QA benchmarks it reaches an average AUROC of 0.7208, with gains on unseen datasets, different model families and scales, and both PPO and GRPO.

Agentic reinforcement learning shifts the reasoning trajectories of large language models, and trajectory-based confidence estimators trained on a base model lose much of their ranking ability after that shift. StaR-TTA treats trajectory stability as a label-free reliability signal: it learns the link between stability and correctness on labeled base trajectories, then uses stability rankings from unlabeled RL trajectories to correct confidence at test time. Across four QA benchmarks it reaches an average AUROC of 0.7208, with gains on unseen datasets, different model families and scales, and both PPO and GRPO.

Grammar assessment for international Chinese education is recast as proof-carrying constrained inference: retrieved rules, error spans, minimal repairs and ordinal grades are coupled through one latent evidence graph, and every score-affecting judgment must carry a rule-level support certificate. On public corpora including MuCGEC, PC2G reaches an error-detection F0.5 of 71.4 against 67.6 for the strongest listed retrieval baseline, CRAG-style, while five-level grade MAE falls from 0.57 to 0.42 and evidence faithfulness reaches 91.8%. Those grades are measured against a silver rubric derived from reference edits, not independent teacher ratings.

Grammar assessment for international Chinese education is recast as proof-carrying constrained inference: retrieved rules, error spans, minimal repairs and ordinal grades are coupled through one latent evidence graph, and every score-affecting judgment must carry a rule-level support certificate. On public corpora including MuCGEC, PC2G reaches an error-detection F0.5 of 71.4 against 67.6 for the strongest listed retrieval baseline, CRAG-style, while five-level grade MAE falls from 0.57 to 0.42 and evidence faithfulness reaches 91.8%. Those grades are measured against a silver rubric derived from reference edits, not independent teacher ratings.

In dermoscopic images, hair occlusion, uneven illumination and weak local contrast fragment lesion boundaries, while self-attention tends to emphasize lower-frequency content and leaves those weak borders underrepresented in skip connections. Swin-WaveNet decomposes each encoder skip feature with a wavelet transform, applies selective state-space scanning only to the high-frequency subbands to reconnect interrupted contours, gates shallow high-frequency responses with deeper semantic guidance to suppress hair and artifact structures, and sharpens boundaries during upsampling with high-frequency attention. On ISIC 2018 it reached a mean Dice of 89.69% and HD95 of 23.60 pixels over three runs, close to Swin-Unet, and 91.69% Dice on ISIC 2017 without fine-tuning.

In dermoscopic images, hair occlusion, uneven illumination and weak local contrast fragment lesion boundaries, while self-attention tends to emphasize lower-frequency content and leaves those weak borders underrepresented in skip connections. Swin-WaveNet decomposes each encoder skip feature with a wavelet transform, applies selective state-space scanning only to the high-frequency subbands to reconnect interrupted contours, gates shallow high-frequency responses with deeper semantic guidance to suppress hair and artifact structures, and sharpens boundaries during upsampling with high-frequency attention. On ISIC 2018 it reached a mean Dice of 89.69% and HD95 of 23.60 pixels over three runs, close to Swin-Unet, and 91.69% Dice on ISIC 2017 without fine-tuning.

Two-dimensional infrared spectroscopy has mostly supported classification or coarse parameter inference, while directly recovering the transition-dipole matrix remains a hard inverse problem. Spec2DipoleNet couples level-structure classification with dipole-matrix regression in one physics-constrained multi-task convolutional network, trained on 8000 simulated spectra generated by the non-Hermitian Hamiltonian method. On validation it reaches an RMSE of 0.085, an R-squared of 0.97 and a normalized cross-correlation of 0.98, recovering allowed transitions well while nominally forbidden transitions deviate more.

Two-dimensional infrared spectroscopy has mostly supported classification or coarse parameter inference, while directly recovering the transition-dipole matrix remains a hard inverse problem. Spec2DipoleNet couples level-structure classification with dipole-matrix regression in one physics-constrained multi-task convolutional network, trained on 8000 simulated spectra generated by the non-Hermitian Hamiltonian method. On validation it reaches an RMSE of 0.085, an R-squared of 0.97 and a normalized cross-correlation of 0.98, recovering allowed transitions well while nominally forbidden transitions deviate more.

Rubric construction now filters candidate decompositions by semantics first and then picks the final structure from how criteria behave on six responses of differing quality, reaching the highest pairwise accuracy on five of six preference benchmarks.

Rubric construction now filters candidate decompositions by semantics first and then picks the final structure from how criteria behave on six responses of differing quality, reaching the highest pairwise accuracy on five of six preference benchmarks.