Google AI Releases TranslateGemma: A New Family of Open Translation Models Built on Gemma 3 with Support for 55 Languages - MarkTechPost
<a href="https://news.google.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?oc=5" target="_blank">Google AI Releases TranslateGemma: A New Family of Open Translat
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RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on single-attempt settings, overlooking their capacity for iterative refinement. In this paper, we present RefineRL, a novel approach designed to unleash the self-refinement capabilities of LLMs for CP problem solving. RefineRL introduces two key innovations: (1) Skeptical-Agent, an iterative self-refinement agent equipped with local execution tools to validate generated solutions against public test cases of CP problems. This agent always maintains a skeptical attitude towards its own outputs and thereby enforces rigorous self-refinement even when validation suggests cor
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arXiv:2604.00788v1 Announce Type: new Abstract: This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended goals. Specifically, we evaluate whether frontier models sabotage safety research when deployed as coding assistants within an AI lab. Applying our methods to four frontier models, we find no confirmed instances of research sabotage. However, we observe that Claude Opus 4.5 Preview (a pre-release snapshot of Opus 4.5) and Sonnet 4.5 frequently refuse to engage with safety-relevant research tasks, citing concerns about research direction, involvement in self-training, and research scope. We additionally find that Opus 4.5 Preview shows reduced unprompted evaluation awareness compared to Sonnet 4.5,
CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detection
arXiv:2604.00716v1 Announce Type: new Abstract: Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning when duplicated at inference time. Finding these circuits currently requires brute-force sweeps costing 25 GPU hours per model. We propose CircuitProbe, which predicts circuit locations from activation statistics in under 5 minutes on CPU, providing a speedup of three to four orders of magnitude. We find that reasoning circuits come in two types: stability circuits in early layers, detected through the derivative of representation change, and magnitude circuits in late layers, detected through anomaly scoring. We validate across 9 models spanning 6 architectures, including 2025 models, confirming that CircuitProbe top predictions m
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RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on single-attempt settings, overlooking their capacity for iterative refinement. In this paper, we present RefineRL, a novel approach designed to unleash the self-refinement capabilities of LLMs for CP problem solving. RefineRL introduces two key innovations: (1) Skeptical-Agent, an iterative self-refinement agent equipped with local execution tools to validate generated solutions against public test cases of CP problems. This agent always maintains a skeptical attitude towards its own outputs and thereby enforces rigorous self-refinement even when validation suggests cor
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arXiv:2604.00788v1 Announce Type: new Abstract: This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended goals. Specifically, we evaluate whether frontier models sabotage safety research when deployed as coding assistants within an AI lab. Applying our methods to four frontier models, we find no confirmed instances of research sabotage. However, we observe that Claude Opus 4.5 Preview (a pre-release snapshot of Opus 4.5) and Sonnet 4.5 frequently refuse to engage with safety-relevant research tasks, citing concerns about research direction, involvement in self-training, and research scope. We additionally find that Opus 4.5 Preview shows reduced unprompted evaluation awareness compared to Sonnet 4.5,
CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detection
arXiv:2604.00716v1 Announce Type: new Abstract: Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning when duplicated at inference time. Finding these circuits currently requires brute-force sweeps costing 25 GPU hours per model. We propose CircuitProbe, which predicts circuit locations from activation statistics in under 5 minutes on CPU, providing a speedup of three to four orders of magnitude. We find that reasoning circuits come in two types: stability circuits in early layers, detected through the derivative of representation change, and magnitude circuits in late layers, detected through anomaly scoring. We validate across 9 models spanning 6 architectures, including 2025 models, confirming that CircuitProbe top predictions m
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