Daily LLM š Latest AI Models News
Joined November 2025
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LLMBRICKS retweeted
š DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous versionāa Pareto improvement in performance and cost. Now live on our website, app, and API.
š ļø Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
1/4
LLMBRICKS retweeted
AI coding agents are getting more powerful. But look at what has happened to their pricing.
In 2022, $10/month was enough to get started with a premium AI coding assistant.
In 2026, developers can spend $100ā$200/month on a single coding agent.
Thatās up to 20 TIMES the monthly spending.
Letās look at the actual numbers. š
2022: The $10 era
GitHub Copilot launched its paid individual plan at $10/month.
Annual cost: $120.
AI helped you autocomplete code, suggest functions, and write repetitive logic.
2024: The $20 era
Cursor Pro was available for $20/month.
Annual cost: $240.
Compared with Copilotās $10 benchmark, thatās 100% more expensive.
But developers were getting something different: AI-assisted editing, codebase context, and increasingly capable coding workflows.
2025: The $200 era
Cursor introduced Ultra at $200/month.
Claude offered Max tiers at $100 and $200/month.
Suddenly, the premium end of the coding-agent market looked very different.
Cursorās new Ultra tier was 900% more expensive than its $20 Pro tier.
Thatās $2,400 per year for one subscription.
2026: The usage-based era
GitHub Copilot introduced usage-based AI credits.
Its subscription tiers now include:
ā¢ Pro: $10/month
ā¢ Pro+: $39/month
ā¢ Max: $100/month
Cursorās established coding plans include:
ā¢ Pro: $20/month
ā¢ Pro Plus: $60/month
ā¢ Ultra: $200/month
Claude Code offers:
ā¢ Pro: $20/month
ā¢ Max 5x: $100/month
ā¢ Max 20x: $200/month
And usage limits still apply.
Now look at the spending difference.
$10/month = $120/year
$20/month = $240/year
$100/month = $1,200/year
$200/month = $2,400/year
Going from a $20 plan to a $200 plan is a 900% increase in subscription spending.
But hereās the important distinction:
These arenāt necessarily price hikes on the same plan.
Companies are introducing higher tiers, more compute, and different usage allowances.
And that raises a bigger question.
Are coding agents becoming more expensive because AI is getting more capable?
Or are developers becoming increasingly dependent on tools whose usage costs are harder to predict?
A developer using AI for occasional autocomplete has very different needs from someone running autonomous agents for eight hours a day.
Higher pricing can make sense when it delivers substantially more value.
But the future of AI coding shouldnāt be accessible only to developers who can afford $200 every month.
In 2022, the conversation was about whether AI could write code.
In 2026, the conversation is about how much developers must pay to let AI keep writing code.
Thatās a remarkable shift in just four years.
Where do you think coding-agent pricing goes next: cheaper access, more expensive subscriptions, or fully usage-based billing?
LLMBRICKS retweeted
"Stateless Language Agents: Scaling Long-Horizon Automated Research"
Long-running AI research agents waste tokens revisiting old ideas or repeating experiments.
Stateless Language Agents (SLA) instead store research progress externally, giving each agent a fresh, focused context while an Advisor coordinates experiments.
This setup outperforms competing frameworks on software engineering and optimization tasks, matching the strongest baseline on one kernel optimization task with 93% fewer tokens.
alphaxiv.org/abs/2610.07625
LLMBRICKS retweeted
.@SakanaAILabs and @berkeley_ai dropped something interesting for recursive self-improvement.
They proposed Multi-Agent Self-Supervision (MASS) - a method that tries to get more out of the modelās own feedback by having copies of it work as a team.
The agents share the same model weights but get different responsibilities, instructions, and contexts ā
The idea is to optimize what each agent receives, produces, and passes on.
Hereās how it works:
āŖļø MASS alternates between two loops.
1. The inner loop improves the workflow:
The model proposes a workflow, runs it, evaluates the results, and revises instructions, task assignments, and information routing. The weights stay unchanged.
2. The outer loop trains the model:
Optimized workflows generate trajectories: records of the agentsā conversations and actions. The shared model is fine-tuned on:
- Subagent trajectories: completing focused tasks
- Orchestrator trajectories: delegating, verifying, and integrating work
And as always, the results:
- 1.2ā1.6Ć better score per token on four research benchmarks after two MASS cycles
- Evaluator accuracy: 73% ā 93%
- 68.3% win rate vs. 64.0%, using fewer training tokens
- Slightly worse score per token on coding and terminal tasks
But the study showed only two rounds of improvement, not endless self-improvement.
But the study showed only two rounds of improvement yet, not endless self-improvement.
Either way, thereās an important insight here: a model really can improve by learning to split work across its copies and share results.
LLMBRICKS retweeted
Multi-Stream Attention Residuals
Multi-Stream Attention Residuals applies multi-head attention over network depth, achieving a 1.58x compute speedup over standard residuals at equal loss in tests on Mixture-of-Experts models up to 28B parameters.
arxivb.org/abs/2610.13630
LLMBRICKS retweeted
Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. āØ
It launches with two core designs: š„³
- Elastic Scheduler: reallocates GPUs between rollout & training as demand shiftsālive executions never interrupted. Up to 1.85Ć speedup over Colocate, 1.78Ć over Async.
- Trajectory Processor: turns branching executions (compaction, sub-agents, retries) into prefix-shared trajectory trees with bounded, deduplicated training samples. Partial progress after timeout still gets scored.
Scaled to Qwen3.8-2.4T-A95B with ~700K tokens per rollout, QwenGyre lifts NL2RepoBench from 52.5% to 58.5% in 48 steps, cutting end-to-end training time by ~44% vs Async.
š Read the paper:
arxiv.org/abs/2609.33848
LLMBRICKS retweeted
New paper from the team at Sakana AI:
Recursive Self-Improvement through Multi-Agent Self-Supervision (MASS)
How do we keep improving AI when the tasks become too complex for humans to reliably evaluate?
For open-ended research, we eventually hit a "supervision bottleneck." If we use the model itself as the solver, optimizer, and evaluator, it gets trapped in a homogeneous loop, reinforcing its own blind spots rather than correcting them.
To break this loop, we drew inspiration from Marvin Minskyās Society of Mind. What if you prompt a single model to organize copies of itself into a structured team, then distill what the team learned back into the base model?
MASS alternates between two loops:
1/ Workflow Optimization: the model iteratively evolves its own multi-agent workflows, figuring out how subagents should communicate and route information to each other.
2/ Workflow Internalization: fine-tune the shared model on the successful multi-agent trajectories so the coordination behavior gets baked into the weights.
After two cycles with a 27B open-weights model, performance improved 1.2-1.6x per output token on tough research benchmarks (MLR-Bench, ScienceAgentBench, DSBench).
Multi-agent traces were also much better training data than single-agent ones. The model learns faster from jointly learning high-level orchestration and bounded subagent execution. The results showing that the model can bootstrap its own coordination skills by organizing copies of itself into teams feels promising as a mechanism towards self-improvement.
Blog: pub.sakana.ai/mass/
Paper: arxiv.org/abs/2610.12176
Incredible work by Hyunin Lee, Jinglue Xu, Jeffrey Seely, Yujin Tang, and our collaborators from UC Berkeley (Somayeh Sojoudi, Matei Zaharia, Donghyun Lee)!
Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence
Blog: pub.sakana.ai/mass/
As AI systems tackle increasingly open-ended research problems, human experts may struggle to assess results and guide further progress. The model itself may be the best available optimizer and evaluator. The challenge is obtaining useful self-supervision within that homogeneous loop, where a model risks reinforcing its own blind spots rather than correcting them.
We ask whether a model can learn from the collective intelligence of its own team.
We introduce Multi-Agent Self-Supervision (MASS). One shared model solves tasks through a team of virtual subagents, searches for better multi-agent workflows, then uses its own judgments to select the best ones.
Training on the best teamās executions provides self-supervision by distilling the teamās collective experience into the shared model.
We ran two cycles using a 27B open-weights model on synthetic open-ended research tasks. After the second cycle, score per output token reached 1.2-1.6x the base model's level across four research benchmarks.
Three key results:
First, MASS enables role generalization within an RSI loop: training only on task-solving data from the optimizee naturally improves the capabilities of both the optimizer and the evaluator.
Second, multi-agent trajectories are more efficient training data than single-agent trajectories for RSI.
Third, optimizer capacity, rather than evaluator capacity, can be the bottleneck in the speed of improvement.
We believe the community needs to investigate homogeneous RSI carefully to support AI safety. When a model is also its own evaluator, shared blind spots could allow mistakes to be accepted as progress and reinforced through training. For AI to improve beyond human expertise safely, we need to understand these failures and preserve effective ways to detect them and intervene.
Paper: arxiv.org/abs/2610.12176
Code: github.com/SakanaAI/mass
This work was a collaboration with researchers at UC Berkeley.
