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9 inkuruGuhanga udushya
ArXiv paper proposes milestone-based training for long-horizon LLM agents
MileGPO uses milestone discovery and local evidence to improve credit assignment when training language-model agents on long, multi-step tasks. The authors report state-of-the-art results on ALFWorld and WebShop, but the claims remain limited to the paper’s experiments.arxiv.orgGuhanga udushya
Gupima ibizamini niba abakozi ba LLM bazi igihe cyo kwibuka, kugenzura cyangwa kubaza
Ibipimo bishya bisuzuma niba abakozi-imvugo-yerekana imiterere bahitamo neza igihe amakuru akomoka kumikoranire agomba kubikwa, kugenzurwa, gukoreshwa byigihe gito cyangwa gusobanurwa numukoresha.arxiv.orgGuhanga udushya
Kugenzura Uburinganire bwururimi-Ururimi Icyitegererezo Cyamazi
Hashyizweho uburyo bushya bwo gusuzuma gahunda yo gushyira amazi mumazi manini yerekana ururimi, hibandwa kuburinganire bwindimi.arxiv.orgPolitiki
Stanford AI Index finds AI policy expanding as sovereignty and investment diverge
Stanford HAI’s 2026 AI Index says national AI strategies are spreading, while data-localization rules, state-backed computing capacity and public investment remain uneven across regions.hai.stanford.eduGuhanga udushya
Together AI benchmark: GLM-5.3 trails GPT-5.6 Sol on the first try, wins on retries at half the price
A Together AI analysis of 904 DeepSWE rollouts reports that OpenAI's GPT-5.6 Sol leads on first-attempt coding accuracy while the open-weight GLM-5.3 leads once retries are allowed, at roughly half the cost per attempt.together.aiGuhanga udushya
Preprint proposes a locally tokenized AI model for robust time-series watermarking
An arXiv preprint proposes an AI generative model and watermarking method for more reliable multivariate time-series data after editing. Authors report tests across finance, energy and neuroimaging benchmarks, but the abstract gives no numerical results or evidence of deployment.arxiv.orgGuhanga udushya
Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder
Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open datasets and specialized models have been virtually non-existent.arxiv.orgGuhanga udushya
Time-Series Retrieval for Grounding Multimodal Language Models in Remaining Useful Life
Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks, raising the question of whether they can effectively support prognostics and health management (PHM).arxiv.orgGuhanga udushya
Nepali-English preprint: text-only AI matched multimodal model on out-of-context misinformation benchmark
A new arXiv preprint introduces NepOOC, a 1,090-pair Nepali-English benchmark for detecting misleading captions attached to authentic images. On this dataset, a text-only mBERT model matched the best tested multimodal system, while image-only models performed near chance.arxiv.org
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