RESEARCH & ENGINEERING

Turn model capability
into useful systems.

Dr.Panda’s research page is a home for the questions behind reliable AI products: how models handle context, tools and multimodal inputs through stable interfaces.

RESEARCH THEMESMETHODS & EVALUATION

AREAS OF INTEREST

Questions that shape
the platform.

01 / CONTEXT

Long-running work

Explore how context limits and request negotiation affect practical multi-step tasks.

02 / MULTIMODAL

More kinds of input

Study how language, visual input, image tools and speech can fit into clear developer workflows.

03 / INTEROPERABILITY

Stable interfaces

Make model capabilities easier to adopt through familiar API request patterns.

Our research themes focus on the practical choices that shape dependable AI workflows across models, tools and deployment environments.

RESEARCH NOTES

Evidence before
big claims.

We explore practical evaluation methods for context handling, tool use, multimodal inputs and deployment boundaries. Each question starts with a workload that reflects how teams build.

RESEARCH NOTESResearch libraryMethods & context

Research notes connect model versions, evaluation methods and known limitations to a clear workload.

EVALUATION LENSES

What a useful answer
needs to hold up to.

Strong AI systems combine capable models with thoughtful evaluation, dependable tools and workflows designed around real tasks.

04 / CONTINUITY

Context quality

Does the model keep important constraints when a task spans multiple turns, long documents or a changing plan? Evaluate retrieval, omission and stale instructions separately.

05 / TOOL USE

Reliable actions

Measure whether a model chooses the right tool, supplies valid arguments and recovers from an ordinary failure. A correct final response should not hide an unsafe or repeated action.

06 / MEDIA

Inputs beyond text

Images and audio introduce their own limits: resolution, transcription quality, language coverage and timing. Test the media path independently from the model that summarizes it.

PRIVATE DEPLOYMENT QUESTIONS

Study the whole
request path.

Model quality is only one part of a deployed AI system. Routing, data handling and client behavior shape whether a feature is useful to a team.

07 / OWNERSHIP

Data boundaries

Identify which service receives each input, how credentials are held, and what is retained. Define clear data boundaries, credential handling and retention practices for every service in the request path.

08 / OPERATIONS

Latency & fallback

Record end-to-end response time, timeout behavior and fallback routes. A retry should be visible and should not silently duplicate a costly action.

A PRACTICAL RESEARCH LOOP

Make the next test
answer one question.

09 / DEFINE

Start with a workload

Write down the real task, expected inputs, acceptable latency and what counts as a useful result. Keep the sample representative of the people who will use it.

10 / COMPARE

Hold conditions steady

Use the same prompts, tools and data when comparing routes. Track quality, speed, context use and failure cases together instead of selecting on one score.

11 / REPORT

Share limits as well

Publish model version, date, method and known limitations alongside any future result. If a claim cannot be reproduced, leave it out of the public archive.

A consistent research loop helps teams compare methods, understand trade-offs and turn one practical question into the next useful test.

Turn model capabilityinto useful systems.

Questions that shapethe platform.