What if google did something like other AI labs.. In the near future, could they introduce "Gemini 4 Ultra" competing with astra and fable.. or will it stay flash-lite against luna and sonnet, with pro as it is against astra and fable. I mean the naming would make more sense..

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What if a single forward pass could let a model read two completely different texts at once? Transformers are built from strongly nonlinear pieces: self-attention and layer after layer of MLPs. So the natural intuition is that mixing two contexts into one input should make the output collapse into noise unrelated to either. This paper overturns that intuition. Simply averaging the token embeddings of two texts and feeding the result as a single input still leaves clear traces of both contexts in the next-token distribution. Tested across Pythia, Llama, and Qwen, the true next token from each individual stream lands in the top-10 ranks of the mixed output 30-40% of the time, and within the top-100 ranks 60-65% of the time. Even more striking: this superposition ability isn't something models learn. It's strongest right at initialization and degrades monotonically as pretraining continues, suggesting it's an intrinsic architectural property that training actually erodes. The authors show it can be substantially restored with lightweight fine-tuning on less than 0.025% of the original pretraining data, and they build on this to propose a guided decoding method that generates two independent, coherent continuations from a single forward pass. Title: Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs URL: #LLM# #Transformers#

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What if being buried under a mountain of debt isn't actually all your fault? After gaining a sizable social media following for sharing her years-long journey to pay down her debt, then starting a podcast about personal finance, @RealGirlProject knows a thing or two about being broke and changing her lifestyle to take control of her finances. The co-host of Debt Heads joins @chafkin and @svaneksmith on the Everybody's Business podcast to discuss who she thinks is partially to blame for America's debt crisis and why she has stopped trying to get to debt zero. Listen and watch at

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What if letting an agent evolve its own workflow just meant it got better at gaming the test? This paper tackles exactly that overfitting problem. Title: RRSI: Regularized Recursive Self-Improvement of Agent Harnesses URL: ❓ What's a harness? It's everything wrapped around the frozen LLM: prompts, control flow, tooling, memory, context management. The same model can perform very differently depending on this design. ❓ Why does self-improving it overfit? Three failure modes show up: fitting too tightly to the evolve-set benchmark, chasing noise, and letting complexity pile up unchecked. 💡 How does RRSI fix it? It applies classic ML regularization (L0, L1, L2) to harness evolution: an annealed edit budget limits changes per round, benchmark-specific proposals get screened out at selection, and unproductive components get pruned. 💡 What's the payoff? Across 8 benchmarks, RRSI beats baselines by up to 22.9% on unseen tasks, while using 30% fewer tokens. It feels like a genuinely grounded step toward agents that can safely improve their own workflow in production. #AIAgents# #SelfImprovement#