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, without requiring extensive expert supervision for each new problem.” Mythos Preview’s announcement says: “We then invoke Claude Code with Mythos Preview, and prompt it with a paragraph that essentially amounts to “Please find a security vulnerability in this program.” … Engineers at Anthropic with no formal security training have asked Mythos Preview to find remote code execution vulnerabilities overnight, and woken up the following morning to a complete, working exploit.” Hiverge says, in an

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uman direction. Left: Fraction of PRs that have been merged by the end of each simulation. Right: The median agent’s degree of code sharing in each simulation. Both metrics are averaged over the three different prompt types for varying simulation size. Only Sonnet 5 is able to maintain both a high merge fraction while directly collaborating and sharing code with other agents. PR progress over the course of a 12 hour simulation for each of five different models. Sonnet 4.6 and Opus 4.6 do a terri

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h PCPs. AMIE (Video) also matched or exceeded AMIE (Text) on these dimensions. Strength in physical observation and examination: AMIE (Video) was rated significantly higher, on average, than both PCPs and AMIE (Text) eliciting physical signs and proactively guiding patient actors through virtual examination maneuvers. This advantage was also reflected in case-specific perception and examination rubric scores. Patient actors preferred the video experience: Patient actors strongly preferred the sy

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ferent no-memory baselines (79.8 vs 72.0 TGC overall) because of the prompt-template difference above. More from this author Your Agent Aced the Task. Will It Do It Again? 118 September 15, 2026 IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license 60 September 9, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment +21

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, but not a complete reproducible environment. Run each cache configuration independently and stop its server before starting the next one. DeepSeek-V4 Inkling Figure 4 reports average TTFT and prompt-token cache hit rate by round. For each round, the hit rate is the sum of cached prefix tokens across requests divided by the sum of their complete prompt lengths. In both workloads, L1 loses reusable prefixes first, L2 delays the capacity limit, and L3 remains high after warmup and finishes above

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September 29, 2026 Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem 33 September 21, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment +30 Models mentioned in this article 6 MultiverseComputingCAI/Hypernova-60B-2605 meta-llama/Llama-3.1-8B-Instruct Papers mentioned in this article 1 Collections mentioned in this article 1 Qw

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cience and dermatology at Stanford University. These results underscore the importance of building AI systems with users in mind and of developing explainability methods that encourage critical thinking rather than overreliance on the model, the researchers say. “It’s getting obvious that we cannot just assume a good AI will solve all problems. We need to pay careful attention to the users who will be using the AI system, because the same explanation can help an expert and mislead a beginner. Of

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del: “no we don't want to change the targets [...] agian [sic] we need to find something that worth [sic] publishing”; That night, we sent one final message offering words of encouragement: “again we are not looking for low hanging fruit, we want proper research to find genuinly [sic] hard findings.” Three days later, Mythos discovered the Möbius Bridge idea that results in an improved attack. A few days after that, and after Claude output a total of one billion output tokens, it had refined the

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id surprise. What would look like a discontinuous jump is revealed to be gradual progress in several necessary, but not sufficient, sub-tasks. The sub-task view also surfaces encouraging signs. A trend we're seeing when reading Fable 5's submissions is that the model is doing local analysis before submitting its implementation. In one submission, the model calculated the drone's camera extrinsics by analyzing a video from the simulation, estimating the camera tilt to within four degrees of the t

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s as observational physiological evidence that align with their reported symptoms. Wearable biosignals in the days leading up to a SymptomAI conversation relative to a historic average from a baseline period across day -30 to -15 for the infected ( red ) and baseline ( gray ) cohorts. The infected cohort includes participants which SymptomAI diagnosed with a respiratory infection while the baseline includes all other participants in our dataset. Day 0 denotes the date of the SymptomAI conversati

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optimization while the quantum system performs calculations. A key area is the use of AI in solving the challenge of error correction in quantum computing. Qubits are notoriously fragile and can essentially fall apart – or decohere – due to environment factors around them, from noise and temperature to light and the activity of other qubits. This can lead to errors, and error-prone quantum systems are neither practical nor useful. AI For QEC A lot of work is being done to see how AI – both gener

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board Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning 22 September 30, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment Upvote 34 +22 Spaces mentioned in this article 1 Running Agents 34 Real World VoiceEQ Benchmark 🎙 34 Explore speech AI model leaderboards with sortable heatmap tables Papers mentioned in this

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rly unchanged. The 2x per-token premium turns out to be the wrong number to look at. The cost of an agent is dominated by how many turns the lead model takes, how much context it drags along, and above all, what it decides not to do itself. The difference comes down to management style: Opus behaves like a micromanager with an intern; Fable is a manager with a capable engineer. The setup A quick refresher on how Fusion’s sidekick architecture works. The lead agent owns the session: it talks to t

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prompt the model, so we never generate images,” Suriyakumar explains. The researchers capture those modifications at multiple time points within the model’s inner structure and average them to summarize how the LoRA adaptor changed the model’s computation. They found these responses to be a strong signal of how a model had been specialized. They tested their method on variations of three types of models, comparing the results to ground-truth data from LoRA adaptors known for generating CSAM, oth

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3 shows, the difference remained bounded across more than 100 steps and repeated weight updates, without sustained upward drift or a sharp increase after updates. This is an encouraging bring-up result, not a final threshold. Figure 3. Train-versus-rollout absolute log-probability difference over the first 100 training steps. Beyond bounded log-probability agreement, the online reward also improved over training. In an extended run, the online raw reward showed a clear upward trend rather than

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hrough self-supervised reconstruction, building on the LSM-2 approach and its Adaptive and Inherited Masking (AIM) framework. This is a crucial design choice, because missing and fragmented data (e.g., stretches of time where data is not available) is the norm with wearable devices, caused by a variety of factors such as sensors’ power-cycle, devices coming off the wrist, power saving modes of operation, and sensors switching on and off. Conventional self-supervised methods assume complete, unin

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gap in reliability is closing with each model generation. Composite score on Embody (our benchmark suite) by model, stacked by control interface. The composite is a normalized average across every robot body ("embodiment") and task in the suite except for high-level locomotion; higher bars mean broader physical competence. Summary of findings A model's robotics score depends as much on the robot body and the control interface as on the model itself. The same model can look weak or strong depend

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; Execute and evaluate the new workflow; Add it back to the tree if the new workflow shows improvement within a budget of $N$ rounds; Repeat steps 2-5 and stop when the top-$k$ average score plateaus or hit the budget Experiments of AFlow in QA, code, and math tasks showed decent improvement of AFlow over manually designed workflows and ADAS. Self-Improving Harness Either context engineering or workflow design is only one part of a harness. We need to search through the entire design space and o

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ber 15, 2026 IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license 60 September 9, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment Upvote 27 +15 Datasets mentioned in this article 1 ibm-research/ScarfBench Updated May 22 • 116 • 19 Spaces mentioned in this article 1 Running 2 ScarfBench 🐠 2 Java framework migration

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l to draft tokens, we append a lightweight Transformer head, the MTP head, to the final layers of the main model. This architecture, which uses a deep exit layer for drafting, leverages the work already performed by the main model’s backbone. The MTP head takes the final high-dimensional activations (hidden states) of the main model and uses them to autoregressively predict a sequence of future tokens. While MTP heads are commonly pre-trained in tandem with the backbone — such as in our recent r

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