Top Tweets for #ChemCensor
2.6B parameters beating purpose-built models in single-step retrosynthesis! π
High time to ensemble these fast SOTA LLMs with search tree algorithms to power the ultimate multi-step retrosynthesis engine. #DDDBench #ChemCensor
πͺΆ What can a 2.6B-parameter language model achieve in retrosynthesis?
With a leak-proof, broad-coverage drug discovery benchmark #DDDBench, it becomes easier to evolve general-purpose models into SOTA-level specialists.
#insilicoSOTAFM
Weβve updated the #DDDBench with our own models, including LFM2-MMAI-Chem-SSRS-2.0, our single-step retrosynthesis specialist and the current SOTA on #ChemCensor.
BTW, our specialist model C3LM is SOTA on single step retrosynthesis! We share the treasure details from data for SFT and rewards for RFT
You only need 2.6B params for the task thanks to efficient @liquidai LFM architecture!
Also, our key simulation engine #ChemCensor is public
Iβm excited to share the follow-up paper to our #ChemCensor framework for evaluating single-step retrosynthesis π§ͺ
Our C3LM model was already competitive with, and in some cases superior to, general-purpose foundation models, but a gap remained between LLMs and conventional retrosynthesis models.
We finally closed and surpassed that gap π
The key ingredients wereβ¦ π
4/n
π Read the paper: https://t.co/JXVfb3PmCQ
π€ Join the discussion on Hugging Face: https://t.co/3YqsGCX1aP
π Recent #ChemCensor benchmark results on #DDDBench: https://t.co/5MmJq51CjX
Kudos to team @sumrexromanus @chem_logos @chemcensor @mathieu_reymond @VladAladin @AlexAliper @biogerontology
Iβm excited to share the follow-up paper to our #ChemCensor framework for evaluating single-step retrosynthesis π§ͺ
Our C3LM model was already competitive with, and in some cases superior to, general-purpose foundation models, but a gap remained between LLMs and conventional retrosynthesis models.
We finally closed and surpassed that gap π
The key ingredients wereβ¦ π
LFM2-2.6B was an excellent base model for our #ChemCensor paper! Looking forward to exploring what LFM2.5-2.6B can unlock with its improved capabilities.
Today we release LFM2.5-2.6B, an agentic model that runs entirely on-device. It plans, calls tools, and works through multi-step tasks on phones, laptops, PCs, and robots. Data never leaves the device, and the marginal cost of each run is essentially zero.
> Pre-trained on ~34T tokens
> LFM2.5 flagship hybrid architecture
> Context length: 128K
> Vocab size: 128K
> balanced intelligence per watt
> customizable on a single GPU for any specialized task
> LFM2 open-weight license
Comparable or better scores compared to models up to nearly 4x its size:
> ToolSandbox 77.83, ahead of Qwen3.5-9B at 76.44
> Multi-IF 80.07, ahead of Gemma-4-E4B-it at 77.35
> IFStruct 85.49, ahead of Qwen3.5-9B at 78.50
π§΅
At @InSilicoMeds, we built #URSA to benchmark retrosynthesis closer to how chemists evaluate routes. Kudos to @sumrexromanus !
#ChemCensor #ICML2026 evaluates reaction plausibility. URSA then scores full routes hierarchically using Solv-N metrics: from formal validity (Solv-0 π΄) to reaction-center logic (Solv-1 π ) and functional-group compatibility (Solv-2 π’). For more details: read our preprint: https://t.co/zY525e6B3n
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