LLMs have high diagnostic accuracy when challenged with published nephrological case scenarios, and the interpretation is limited, however, because previous exposure of the models to individual vignettes, or parts thereof, cannot be ruled out.
Chalid Hasan, Göran R. Boeckel, S. Siam et al.· Deutsches Ärzteblatt Interna...· 0 citations
Abstract Data combining field observations with information from farm sensors, remote sensing, weather services, agricultural machinery, mobile apps, and e-commerce are increasingly underpinning new, intelligent ways of supporting agriculture decisions. This paper reviews the evidence for improvements in farm productiv...
Abstract Data combining field observations with information from farm sensors, remote sensing, weather services, agricultural machinery, mobile apps, and e-commerce are increasingly underpinning new, intelligent ways of supporting agriculture decisions. This paper reviews the evidence for improvements in farm productiv...
Abstract Four small language models of 124M–1.1B parameters were trained on the same five-task stream with five configurations: full fine-tuning, LoRA (r=8 and 16), bottleneck adapters, and LoRA r=8 with 2% replay. We evaluate task accuracy, backward transfer, forgetting, forward transfer, and learning plasticity. Acro...
Vedant Rajendra Vare, Jatin Nayak, Mihir Jadhav et al.· Zenodo (CERN European Organi...· 0 citations
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Abstract Four small language models of 124M–1.1B parameters were trained on the same five-task stream with five configurations: full fine-tuning, LoRA (r=8 and 16), bottleneck adapters, and LoRA r=8 with 2% replay. We evaluate task accuracy, backward transfer, forgetting, forward transfer, and learning plasticity. Acro...
Vedant Rajendra Vare, Jatin Nayak, Mihir Jadhav et al.· Zenodo (CERN European Organi...· 0 citations
Bias auditing should be treated as an ongoing, multi-vendor process, rather than a one-time assessment, because gender-related biases are common in LLMs, but their direction and magnitude are highly heterogeneous, to the point that some models behave in diametrically opposite ways to others.
On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD treats all teacher signals equally, assuming that the teacher's supervision is equally important for every token. However, teacher signals at different tokens may have v...
Zhen-Yu Wang, Tian-Ze Wang, Lin-Jun Zhang et al.· 0 citations
MIST, the Misleading-Image Stress Test: 200 English sentences, each built around a phrase readable either figuratively or literally and shown with an aligned image depicting its reading, a misleading image depicting the opposite, or no image at all is introduced.
Nagham Omar, Mahmoud Jabarin, Kinan Ibraheem et al.· 0 citations
This paper revisits the standard state-only formulation of value estimation and proposes PPO, a self-privileged actor-critic framework that consistently improves value-estimation quality by a substantial margin and outperforms representative actor-critic and critic-free RLVR baselines on challenging mathematical reason...
Kun Liang, Chenming Tang, Clive Bai et al.· 0 citations
Jev answers MMLU's calculation-heavy questions more accurately than other MMLU questions (94% vs. 91%), whereas both open models, and all three on C-Eval, find them harder.
Tobias Deußer, L. Sparrenberg, R. Sifa· 9 citations· ⚡1
The results show that source-conditioned audio generation can relax the conventional dependence of speculative drafting on the target's evolving output prefix, exposing substantial parallelism for efficient inference.
Yun-Zhe Li, Kyoungjun Park, Hong-Zi Zhu et al.· 0 citations
Test-time compute scaling has emerged as a cornerstone of advanced machine reasoning, yet performing iterative deliberation directly within continuous latent representation spaces reveals a catastrophic pathology: the Deliberation Drift Cliff. While unconstrained recurrent latent models achieve initial reasoning gains...
