Selected from source-grounded bilingual updates that are complete enough to check. Briefs are arranged automatically; items are AI-generated and not individually human-reviewed by default (human-reviewed items are labeled), so verify original sources before citing.
How this brief is selected: we include AI4Science updates with reliable sources and complete Chinese and English coverage, prioritizing fuller and easier-to-check information.
01Discovery · LILA Sciences
AI Models & Platforms Lila Sciences’ AI Lab Uncovers Palladium Catalysts for Green Hydrogen Add Unite.AI to your preferred sources on Google Lila Sciences said on September 25, 2026, that its AI-directed laboratory proposed, synthesized and screened 2,942 catalysts for green hydrogen in three months, identifying six palladium-based material families for the acidic oxygen evolution reaction at the heart of hydrogen production. The research team detailed the work in a preprint submitted to arXiv on September 24, 2026: “AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution,” by Ken J. Jenewein and 20 other authors. Producing hydrogen from water requires two half-reactions: the hydrogen evolution reaction at the cathode and the oxygen evolution reaction, or OER, at the anode. OER runs at high potentials in strongly acidic environments that dissolve most metals. In today’s commercial electrolyzers, iridium oxide is the catalyst best able to survive those conditions while remaining active; ruthenium is a less stable option. Both are among the rarest elements on Earth, and nearly all iridium is produced as a byproduct of platinum mining, at only a few tonnes per year worldwide. The Palladium Result While screening 2,942 oxides across 53 material systems and 26 elements, the platform began synthesizing and testing palladium compositions. According to the company, palladium had been considered a dead end for OER because it was assumed to underperform in acidic conditions. The model’s candidates were modified palladium compositions rather than the pure metal, with small additions of other elements transforming the metal’s behavior. During the second and subsequent campaigns, the program identified six palladium-based material families on or near the Pareto front for OER, meaning an optimal balance of activity and stability. After repeated activity tests and more than 1,000 hours of stability testing, Lila said the top metal composition performed comparably to ruthenium, an industry-standard OER catalyst. John Gregoire, the company’s chief autonomous science officer, said his former group at Caltech explored catalysts for this reaction for 12 years and that he would have advised a student proposing this combination of elements to pursue a more promising direction. “This combination defies all t…
Science · Science Podcast
First up on the podcast, Staff Reporter Tom Howarth joins host Sarah Crespi to talk about the language artificial intelligence agents evolve inside their swarms . Next on the show, producer Elah Feder talks with Revathe Thillaikumar , a behavioral ecologist and postdoctoral fellow at the Max Planck Institute of Animal Behavior. The two discuss the value of social learning as baby orangutans learn to find their own food over the longest dependency period of any animal, except humans. Finally, from our series on scientific autobiographies, books host Angela Saini talks with astrophysicist Sara Seager about the moving account of life after losing her husband in her book, The Smallest Lights in the Universe . This week’s episode was produced with help from Podigy . About the Science Podcast Image: inkoly/istock Learn more about your ad choices. Visit megaphone.fm/adchoices
AIhub · Research news and interviews
What happens when you shrink a language model twice — once by removing some of its weights, once by storing what’s left with fewer bits? You’d guess the damage just adds up. It doesn’t. The problem: does compression just add up? Small language models are increasingly used for on-device apps that need to run in tight memory budgets [Zhou et al. 2025]. To shrink them enough, people often combine two tricks: pruning, which removes some of the model’s weights, and quantization, which stores the remaining weights using fewer bits. Recent work has combined both, often finding the mix beats either trick alone [Kang et al. 2025]. But most of these studies only look at the combined result — they don’t ask whether the two tricks are simply adding their effects together, or doing something else entirely. We wanted to find out. A simple test: does it just add up? To check, we borrowed a simple idea from statistics. For each benchmark, we measure four things: the original model, the pruned-only model, the quantized-only model, and the hybrid. Then we compare: If the two tricks were simply adding up, would land close to zero. If is positive, the hybrid does better than expected. If it’s negative, the hybrid does worse. We tested this on three small models — Falcon3-1B-Base, LLaMA-3.2-1B, and a quick check on Qwen2.5-1.5B — at different pruning levels and with both 4-bit and 8-bit quantization, all on a single consumer GPU (an RTX 3080 Ti). What we found: truthfulness up, reasoning down The results split sharply by task. On TruthfulQA, the hybrids consistently beat expectations — by about +7 points on Falcon and +12 to +14 points on LLaMA. It’s as if combining the two tricks accidentally made the models more careful about truthfulness. On reasoning and knowledge tasks, the opposite happened. GSM8K (grade-school math) and MMLU (general knowledge) dropped far more than expected — by 16 to 20 points on Falcon. In several hybrid setups, GSM8K accuracy fell all the way to zero. The twist: more pruning doesn’t mean more damage We assumed these effects would get worse the more we pruned. They didn’t. Instead, they peaked at moderate pruning (20–30%) and faded back toward normal at 50% pruning — both the TruthfulQA boost and the reasoning drop. More pruning didn’t mean more damage; it meant the surprise effect faded. We also found the order barely mattered — quantizing before pru…
