Research and tools are exposing gaps in LLM risk calibration, empathy control and jailbreak robustness, while mechanistic interpretability becomes more accessible. Educational and clinical deployments are beginning to add governance frameworks for these limitations.
A new white-box fuzzing framework replaces response-level feedback with a continuous score derived from safety neurons during prefill. Across 21 models it discovers jailbreaks in 76-100% of cases and transfers optimized templates to open-weight and p...
A neuro-symbolic framework combines a Transformer predictor with F-Logic rules to identify at-risk students in online education. On OULAD it reaches accuracy 0.900 and F1-score 0.894, with average detection at week 9.32. The key difference is explain...
A study on three instruction-tuned LLMs shows that a decodable empathy direction produces only partial shifts in automated scores. The affective control passes, but the cognitive instrument is too coarse. Gemma's Recognition ablation lowers the class...
Silico brings mechanistic interpretability tools previously reserved for elite labs to researchers and startups. The bet is that understanding models from the inside makes them safer, more controllable, and better suited to local deployments.
At Cheshire Academy, teachers and students experiment with ChatGPT, Perplexity and MagicSchool under traffic-light rules: green for unrestricted use, red for a ban. The school trains staff on LLM limits and tests a Student AI Council on healthy AI us...
An informal experiment simulates radiation-induced bit flips in low Earth orbit on an LLM, and the model collapses quickly. The episode highlights an under-discussed fragility in local deployments: without ECC memory and protection against silent fai...
LLM-based therapy apps and general chatbots understand 76-82% of adolescent vocabulary but correctly calibrate only 64-72% of clinical risk. The 10-14 point gap, absent in human therapists, widens with ambiguity. Six failure patterns compound, lightw...
