Open-Source Models & Weights

Decision models can achieve order-invariant option scoring without sacrificing accuracy by treating each option independently, bypassing the need for complex sequence modeling.

Latent LLM personas persist in weights post-training, readily resurfacing with specific prompts, and standard unlearning methods are insufficient to remove them without performance degradation.

Standard information retrieval methods can match LLM-mediated skill selection for agents, slashing per-task costs from $51 to $27.

Distilling real GPT-4o-labeled reports, not synthetic data, enables open-weight models to match proprietary LLMs for high-value clinical entity extraction.

Forbidden capabilities can still be learned even when suppressed in current outputs, as internal gradients enable future acquisition, but gradient sealing effectively blocks these pathways.

Evaluations across seven tasks and three Qwen scales show higher mean accuracy than RandOpt on Countdown, GSM8K, and MATH-500 at every scale, and perturbation geometry is highlighted as a key design choice for population-efficient, gradient-free search around pretrained models.

A diagnostic framework that measures point accuracy alongside dispersion retention, the ratio of predicted to human standard deviation, on 10,000 respondent--question pairs from the World Values Survey (WVS) spanning twelve countries and six continents is developed.

An integrated robotics course framework that organizes these activities around a shared robotic artifact, and offers a structured approach for adapting robotics research workflows into interdisciplinary precollege courses.

This work shows that Kimi Delta Attention (KDA) can realize 2D rotations by combining a single delta-rule transformation with a second reflection supplied by its channel-wise gate, and characterize the expressivity of CKDA and prove that every orthogonal diagonal-plus-rank-one matrix is exactly a CKDA transition matrix.

Control experiments on three cybersecurity datasets covering MITRE ATT&CK network traffic, cyber threat intelligence (CTI), and CVE vulnerability analysis show that reasoning topology substantially affects performance, and Graph reasoning achieves the highest overall accuracy.

A fault-class-matched, layout-aware, width-tiered oracle family: a layout/permutation contract checker and a contract-level metamorphic relation (MR-1) for the metadata channel, a global-phase tracker for the phase channel, and a determinism runner for reproducibility.

A large-scale reproducibility study to systematically investigate the impact of semantic ID design on generative recommendation under a unified experimental framework provides a controlled and reproducible understanding of semantic ID design and offers practical insights for future generative recommender systems.

Probe of Internal Recognition (PIR) separates a model that will not answer from one that cannot, which supports sandbagging audits and unlearning verification, and extends from multiple-choice questions to free-form generation.

This study introduces TrustReviewer, an open-source LLM-based system for generating peer reviews of AI and machine learning papers and describes a concrete risk of recursive reviewer training and provides practical interventions for preserving judgment diversity and improving recommendation alignment in AI-assisted scientific evaluation.

A 202-scenario benchmark with Reference Decisions under a seven-class taxonomy is introduced and Decision Alignment and safety-specific error metrics are evaluated, finding structured LLM decisions are insufficient as a standalone safety mechanism and deployment requires an independent enforcement layer.

Inspired by the efficient communication principles of federated learning (FL), this work introduces two hybrid algorithms - FL+FSDP and FL+HSDP - interleaving sharded DP with FedAvg-style aggregations, demonstrating superior computational efficiency and improved model quality.

SwitchSD is introduced, an adaptive framework that treats copying as a latent control signal of the LLM that allows the system to dynamically switch between neural drafting and context-based copying, effectively turning copying from a noisy heuristic into a principled, model-aware decoding regime.

FP-Sketch is presented, a verifier that runs after an unmodified tensor core GEMM whose half precision operands are accumulated and delivered at FP32, and its constant depends on the BLAS and the operand format and must grow as $n^{2.57}$ for a square product.

These findings verify the original paper's claim that User Profile Recommendation achieves competitive performance under its test-set reranking protocol and makes recommendations more transparent.

SeetaPsych v1.0 is presented, an open source, unified and extensible computer vision toolkit designed to extract psychologically relevant signals from facial images and/or face based videos to offer an integrated and accessible visual measurement platform for research in psychology, behavioral science, human computer interaction, and related fields.