Salesforce AI Research

Salesforce AI Research has open-sourced GIFT-Eval-CTX, a benchmark for whether forecasting systems can turn forward-looking business context into accurate numerical predictions. Traditional time-series foundation models take numerical history and predefined variables, so they struggle to use known future events such as heatwaves, road closures or software releases. The benchmark splits context into macro trends and micro events across 10,000 tasks from 20 real-world datasets; adding structured future context cut GPT-5.6 Sol's error by 40.7% and Claude Opus 4.8's by 29.6%.

Salesforce AI Research has open-sourced GIFT-Eval-CTX, a benchmark for whether forecasting systems can turn forward-looking business context into accurate numerical predictions. Traditional time-series foundation models take numerical history and predefined variables, so they struggle to use known future events such as heatwaves, road closures or software releases. The benchmark splits context into macro trends and micro events across 10,000 tasks from 20 real-world datasets; adding structured future context cut GPT-5.6 Sol's error by 40.7% and Claude Opus 4.8's by 29.6%.

Salesforce AI Research released Interactive Visual Gym (IVG), which moves visual AI assistant evaluation from passively watching pre-recorded video to closed-loop simulation: the assistant watches the user's live feed and speaks guidance, and the simulated user changes its actions in response. The accompanying IVG-Bench covers 315 complex scenarios across three operational environments and evaluates 14 visual assistants; the strongest, Gemini 4 Argon, reaches only 53.4% task success within the time limit, averaged across the three environments. In an early proof of concept, an open-source model trained with the simulator, Gwen 3.5-4B, rose from 33.3% to 42.2% success on entirely new tasks it had never seen during training.

Salesforce AI Research released Interactive Visual Gym (IVG), which moves visual AI assistant evaluation from passively watching pre-recorded video to closed-loop simulation: the assistant watches the user's live feed and speaks guidance, and the simulated user changes its actions in response. The accompanying IVG-Bench covers 315 complex scenarios across three operational environments and evaluates 14 visual assistants; the strongest, Gemini 4 Argon, reaches only 53.4% task success within the time limit, averaged across the three environments. In an early proof of concept, an open-source model trained with the simulator, Gwen 3.5-4B, rose from 33.3% to 42.2% success on entirely new tasks it had never seen during training.

Salesforce AI Research updated the memory policy behind Agentforce Memory so an agent treats a customer's later statement as a correction to a known fact rather than a second fact filed beside it. The evaluation ran on the live Agentforce system, using long multi-session conversations from the public ConvoMem benchmark with specific facts planted along the way, then asked the agent questions that depend on those facts. The updated policy recalls 1.8 times as many facts customers state outright as the policy it replaced, and 2.2 times as many facts that change over time, with nothing underneath the policy changed.

Salesforce AI Research updated the memory policy behind Agentforce Memory so an agent treats a customer's later statement as a correction to a known fact rather than a second fact filed beside it. The evaluation ran on the live Agentforce system, using long multi-session conversations from the public ConvoMem benchmark with specific facts planted along the way, then asked the agent questions that depend on those facts. The updated policy recalls 1.8 times as many facts customers state outright as the policy it replaced, and 2.2 times as many facts that change over time, with nothing underneath the policy changed.

Salesforce AI Research has built Launa-Omni, a voice model for real-time enterprise conversation that keeps speech and language in one audio-adapted text model and uses a separate in-house text-to-speech model to speak the reply. Unlike pipelines that chain speech recognition, a language model and speech generation, speech is not transcribed before reasoning, so pace, hesitation and emphasis survive the first handoff. The model is significantly smaller than several larger voice models and reports competitive results on spoken instruction following, multi-turn dialogue and customer-service tasks; a simplified version already powers Slack's voice dictation.

Salesforce AI Research has built Launa-Omni, a voice model for real-time enterprise conversation that keeps speech and language in one audio-adapted text model and uses a separate in-house text-to-speech model to speak the reply. Unlike pipelines that chain speech recognition, a language model and speech generation, speech is not transcribed before reasoning, so pace, hesitation and emphasis survive the first handoff. The model is significantly smaller than several larger voice models and reports competitive results on spoken instruction following, multi-turn dialogue and customer-service tasks; a simplified version already powers Slack's voice dictation.