Open-Source Models & Weights - Weekly Roundup

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.

It is demonstrated that LLM annotation reproducibility can coexist with poor construct correspondence and motivate validation procedures that distinguish reproducibility, construct validity, and communication context variation when LLMs are used as measurement instruments.

The results strongly support the construct validity of 24 code quality metrics and reveal Coupling and Inheritance are revealed as multidimensional constructs, since they require measuring two different concepts, revealed as sub-categories in the analysis.

The experiments show that self-supervised pretraining and mark localization are the two most impactful factors, with learned cropping recovering the majority of the gain from manual cropping without requiring ground-truth annotations at inference time.

This paper presents an optimized, high-frequency Multispeculative PositMAC architecture for 32- and 64-bits Posit arithmetic, which achieves the highest operating frequency and reduces cycle time by up to 79.0\% with respect to 64-bit quire-enabled alternatives.

FloatLib is the first Lean library to unify IEEE binary and decimal arithmetic, arbitrary-width posits, P3109, and user-defined formats and rounding rules behind interchangeable certified software backends.

This paper proposes and analyzes Comparison-based Preference Optimization (ComPO), a zeroth-order alignment method based on comparison oracles, and establishes a convergence guarantee for its basic offline scheme under smoothness, gradient sparsity, and compatibility between the oracle and a latent objective.

BurnRiSc is presented, a framework that operationalizes the Oldenburg Burnout Inventory's two dimensions, exhaustion and disengagement, as 14 behavioral and linguistic signals computed from GitHub activity and scored against each contributor's own history, as evidence that burnout is screenable from public data.

This work finds that simple difference of means vectors coherently represent reward hacking in Kimi K3, GLM 5.2, and Qwen 3.8 Max, and provides evidence that simple, white-box methods can be used to scalably study and monitor reward hacking behaviors in frontier open source models.

It is found that aggregate pairwise comparisons transfer strongly across evaluation settings, including from randomly generated tasks to clinically meaningful tasks and across datasets, and at the same time, clinically meaningful tasks exhibit greater task-method interaction.

The findings show that even the presence of a small percentage of biased agents in such a system leads to significant shifts in the opinions of non-biased agents, and semantic analysis demonstrates that rhetorical consistency in textual explanations increases systematically with biased exposure and is partially decoupled from numerical convergenumericalutral agents.

A framework to measure the trust profile of an arbitrary LLM based on a variety of payload framings sent through different channels is presented, and cross-channel fragmentation attacks that distribute seemingly benign payloads across two or three channels are devised.

It is argued that local LLM systems need explicit guarantees for prompt lifetime, persistent storage, and tenant isolation, because the surrounding serving software handles prompt data before, during, and after inference.

An SSD-native local MoE inference system that addresses challenges with an SSD I/O pipeline optimized for expert delivery, a native three-tier storage hierarchy that delivers and retains experts dynamically, and balanced CPU--GPU hybrid execution.

An end-to-end computational pipeline for classifying intergovernmental agreements by institutional form and extracting financial relationships between principals and agents in service contracts is introduced, rendering the content of Iowa interlocal agreements analyzable at scale for the first time.

PentestChain is presented, a ten-phase automated penetration testing framework that couples a curated, deterministic exploit map with a cost-aware AI cascade-a local Ollama model, kept off the critical path by a deterministic backbone, sustains end-to-end operation at zero measured paid-API cost.

It is shown that benchmark identity alone is insufficient to reconstruct the evaluated task or determine the appropriate scope of comparison across reported repair rates, so a machine-readable schema for specifying each experimental setting is introduced to improve reproducibility and make the scope of cross-system comparisons explicit.

MeshKV, a KV cache fabric that moves blocks as packetized flows over a lightweight NoC, co-designs TaKV affine striping to spread homes and cut hotspot load and co-designs Mare multicast with verified duplicate suppression, and Pad, which converts bisection back-pressure into useful KV transfer.