From Search to Agency — Two AI Papers That Changed How I Think

Ibbad Ur Rehman — Sat, 14 Mar 2026 16:28:33 +0000

Introduction

When my AI course professor assigned us to read and blog about recent research
papers, I expected to spend a weekend forcing myself through dense academic
writing. What actually happened shifted how I think about two topics I thought
I already understood: search algorithms and intelligent agents.

The two papers I chose:

"A Survey of LLM-based Deep Search Agents" (2026)
"Research on the A* Algorithm Based on Adaptive Weights" (2025)

are not obviously related at first glance. One is about large language models
navigating information, the other is about a classic pathfinding algorithm.
But together, they tell a unified story about what search really means
in modern AI.

Paper 1: A Survey of LLM-based Deep Search Agents (2026)

What is the paper trying to do?

This survey maps out an entirely new category of AI system: the Deep Search
Agent. The authors document how we evolved from:

Traditional keyword search → LLM-enhanced search → Agentic search

In agentic search, an LLM autonomously plans, retrieves, reflects, and
iterates until it can synthesize a comprehensive answer to a complex question.

The core architectural loop the paper formalizes is:

Plan → Act → Observe → Reflect → Generate

Crucially, the Reflect stage decides whether to loop back and search
again or to finalize the answer. Real-world systems like OpenAI's Deep
Research and Perplexity's Pro Search are concrete implementations
of this loop.

The Technical Depth

These agents organize their retrieval using three distinct search strategies:

Strategy	How it works
Sequential	Works through one reasoning thread at a time, step by step
Parallel	Decomposes the question into sub-questions, explores all branches simultaneously
Hybrid	Combines both, sometimes using Monte Carlo Tree Search (MCTS)

The task formulation is a state-space problem: given a user query q,
the agent operates over a trajectory of (observation, action) pairs and must
reach a terminal state where the accumulated evidence satisfies the
information need.

Connection to Our Course

In our AI course, we study rational agents using the PEAS framework
(Performance, Environment, Actuators, Sensors). A Deep Search Agent is
structurally a goal-based agent where:

Goal — answering the query completely
Actuator — ability to query search engines or browse URLs
Sensor — the retrieved text it reads
Environment — the open web (partially observable, dynamic)

The search strategies map directly to what we study in class:

Sequential search = Depth-First Search (DFS)

Parallel search = Breadth-First Search (BFS)

The goal test = the moment the agent decides it has enough information

Paper 2: A* Algorithm Based on Adaptive Weights (2025)

What is the paper trying to do?

The standard A* algorithm has a well-known limitation: it expands a large
number of nodes trying to be both fast and optimal simultaneously.

This paper proposes a solution — a dynamic weighting function based on
a radial basis function — to make A* smarter about when to rush and when
to be careful.

The core formula becomes:

f(n) = g(n) + f(x,y) × h(n)

Where f(x,y) is not a constant but a dynamic weight that changes based
on the agent's position:

Position	Weight	Behaviour
Near start	High	Heuristic amplified, algorithm charges quickly toward goal
Near goal	Low	Algorithm slows down, searches carefully for optimal path

Secondary Optimization

The paper also adds two post-processing steps:

Removing redundant intermediate nodes — eliminates unnecessary waypoints
Bessel curve smoothing — makes the final path practical for real robots and vehicles

Results

Compared to standard A*, the adaptive version reduces nodes searched by an
average of 76.4% and reduces total turn angle by 71.7%. For autonomous
vehicles and robots, this means less computation and smoother, safer paths.

Connection to Our Course

In our lectures on informed search, we study A* as the gold standard —
guaranteed to find the optimal solution if h(n) is admissible (never
overestimates the true cost).

This paper asks a deeper question:

Even within admissible heuristics, can we be smarter about how much we
trust the heuristic at different points in the search?

The answer is yes — and it maps directly to our course concept of
weighted A. What makes this paper genuinely interesting is that the
weight is not a fixed constant chosen by a human engineer, but a function
of the agent's position, computed automatically. The **Euclidean distance
heuristic* we study in class is retained, but modulated dynamically rather
than applied uniformly.

My NotebookLM Experience vs. Manual Reading

I read both papers manually first, and it was harder than expected. The
taxonomy tables in the survey paper required multiple passes. The radial
basis function notation in the A* paper was not immediately clear. But
that struggle built real understanding.

When I then used NotebookLM, it was useful for generating follow-up
questions and locating specific sections quickly — but it simplified
concepts I now knew were more nuanced. It flattened the dynamic weighting
idea into something almost trivial compared to what the paper actually
presents.

My recommendation: read the paper yourself first, then use NotebookLM
to fill gaps and test your understanding — not to replace the reading.

Conclusion

I came into this assignment thinking of search algorithms and agents as
separate textbook chapters. I am leaving it understanding them as two
perspectives on the same underlying problem: how does an intelligent system
efficiently navigate a space of possibilities to reach a goal?

Whether that space is a city map, the open web, or a robot's environment,
the core problem is identical. The adaptive A* paper makes the heuristic
context-sensitive. The Deep Search Agent paper replaces a hand-coded
heuristic with an LLM's judgment about relevance. Both are moving in the
same direction — search that adapts to where it is, rather than applying
the same strategy everywhere blindly.

cc: @raqeeb_26

Paper references:
Xi et al. (2026). A Survey of LLM-based Deep Search Agents. arXiv:2508.05668
Liu et al. (2025). Research on the A Algorithm Based on Adaptive Weights.*

DEV Community: Ibbad Ur Rehman

From Search to Agency — Two AI Papers That Changed How I Think

Introduction

Paper 1: A Survey of LLM-based Deep Search Agents (2026)

What is the paper trying to do?

The Technical Depth

Connection to Our Course

Paper 2: A* Algorithm Based on Adaptive Weights (2025)

What is the paper trying to do?

Secondary Optimization

Results

Connection to Our Course

My NotebookLM Experience vs. Manual Reading

Conclusion